Installing required packages

library(glue)

Attaching package: 㤼㸱glue㤼㸲

The following object is masked from 㤼㸱package:dplyr㤼㸲:

    collapse

Loading the data

```r
blm2020<-read.csv('../../gen/data-preparation/temp/BLM2020_dataset.csv', sep = ' ', na.strings=c(\\, \NA\))

blm2021<-read.csv('../../gen/data-preparation/temp/BLM2021_dataset.csv', sep = ' ', na.strings=c(\\, \NA\))

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EOF within quoted stringError in read.table(file = file, header = header, sep = sep, quote = quote, : duplicate ‘row.names’ are not allowed




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*Show the ratio of replies, retweets and likes*


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```r
blm2020_retweets<-blm2020[blm2020$retweet > 1, ]
blm2020_retweets

blm2021_retweets<-blm2021[blm2021$retweet > 1, ]
blm2021_retweets
blm2020_likes<-blm2020[blm2020$like >1, ]
blm2020_likes

blm2021_likes<-blm2021[blm2021$like >1, ]
blm2021_likes
blm2020_replies<-blm2020[blm2020$reply >1, ]
blm2020_replies

count(blm2020_replies)
blm2021_replies<-blm2021[blm2021$reply >1, ]
blm2021_replies
count(blm2021_replies)

Creating a data frame


data2020<-data.frame(category=c("Retweets", "Likes", "Replies"), count=c(2, 5, 3))
data2020
data2021<-data.frame(category=c("Retweets", "Likes", "Replies"), count=c(707, 1738, 574))
data2021

Visualizing the data 2020

Visualizing the data 2021


#Adding columns
data2021$fraction = data2021$count / sum(data2021$count)
data2021$percentage = data2021$count / sum(data2021$count) * 100
data2021$ymax = cumsum(data2021$fraction)
data2021$ymin = c(0, head(data2021$ymax, n=-1))

#Rounding up

data2021 <- round_df(data2021, 2)

# Specify what the legend should say
Type_of_Tweet <- paste(data2020$category, data2021$percentage, "%")

ggplot(data2021, aes(ymax=ymax, ymin=ymin, xmax=4, xmin=3, fill=Type_of_Tweet)) +
  geom_rect() +
  coord_polar(theta="y") + 
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "right")

Most frequent words in the 2020 tweets

Lets see which words are more frequently used in the posts of 2020

blm2020$text <- gsub("@\\S*", "", blm2020$text)
blm2020$text <- gsub("amp", "", blm2020$text) 
blm2020$text <- gsub("[\r\n]", "", blm2020$text)
blm2020$text <- gsub("[[:punct:]]", "", blm2020$text)

#removing stop words from the text - so the most frequent words are not or at and

tweets <- blm2020 %>%
  select(text) %>%
  unnest_tokens(word, text)
tweets <- tweets %>%
  anti_join(get_stopwords(language = "nl", source = "snowball"))
Joining, by = "word"
#making a histogram
tweets %>% # gives you a bar chart of the most frequent words found in the tweets
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
  labs(y = "Count",
       x = "Unique words",
       title = "Most frequent words found in the tweets of #BLM",
       subtitle = "Stop words removed from the list")
Selecting by n

Most frequent words in the 2021 tweets Lets now see which words are more frequently used in the posts of 2021

blm2021$text <- gsub("@\\S*", "", blm2021$text)
blm2021$text <- gsub("amp", "", blm2021$text) 
blm2021$text <- gsub("[\r\n]", "", blm2021$text)
blm2021$text <- gsub("[[:punct:]]", "", blm2021$text)

#removing stop words from the text - so the most frequent words are not or at and

tweets1 <- blm2021 %>%
  select(text) %>%
  unnest_tokens(word, text)
tweets1 <- tweets1 %>%
  anti_join(get_stopwords(language = "nl", source = "snowball"))
Joining, by = "word"
#making a histogram
tweets1 %>% # gives you a bar chart of the most frequent words found in the tweets
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
  labs(y = "Count",
       x = "Unique words",
       title = "Most frequent words found in the tweets of #BLM 2021",
       subtitle = "Stop words removed from the list")
Selecting by n

Sentiment analysis for positive or negative tweets 2020

# Converting tweets to ASCII to trackle strange characters
tweets_sentiment <- iconv(tweets, from="UTF-8", to="ASCII", sub="")

# removing retweets, in case needed 
tweets_sentiment <-gsub("(RT|via)((?:\\b\\w*@\\w+)+)","",tweets_sentiment)

# removing mentions, in case needed
tweets_sentiment <-gsub("@\\w+","",tweets_sentiment)
ew_sentiment<-get_nrc_sentiment((tweets_sentiment))
sentimentscores<-data.frame(colSums(ew_sentiment[,]))
names(sentimentscores) <- "Score"
sentimentscores <- cbind("sentiment"=rownames(sentimentscores),sentimentscores)
rownames(sentimentscores) <- NULL
ggplot(data=sentimentscores,aes(x=sentiment,y=Score))+
  geom_bar(aes(fill=sentiment),stat = "identity")+
  theme(legend.position="none")+
  xlab("Sentiments")+ylab("Scores")+
  ggtitle("Total sentiment based on scores 2020")+
  theme_minimal()

Sentiment analysis for positive or negative tweets 2021

# Converting tweets to ASCII to trackle strange characters
tweets_sentiment_1 <- iconv(tweets, from="UTF-8", to="ASCII", sub="")

# removing retweets, in case needed 
tweets_sentiment_1 <-gsub("(RT|via)((?:\\b\\w*@\\w+)+)","",tweets_sentiment_1)

# removing mentions, in case needed
tweets_sentiment_1 <-gsub("@\\w+","",tweets_sentiment_1)
ew_sentiment<-get_nrc_sentiment((tweets_sentiment))
sentimentscores<-data.frame(colSums(ew_sentiment[,]))
names(sentimentscores) <- "Score"
sentimentscores <- cbind("sentiment"=rownames(sentimentscores),sentimentscores)
rownames(sentimentscores) <- NULL
ggplot(data=sentimentscores,aes(x=sentiment,y=Score))+
  geom_bar(aes(fill=sentiment),stat = "identity")+
  theme(legend.position="none")+
  xlab("Sentiments")+ylab("Scores")+
  ggtitle("Total sentiment based on scores 2021")+
  theme_minimal()

Visualizing the variables in the dataset 2020

#Making a ggplot
#ploting retweets by location
ggplot(data = blm2020) + 
  geom_point(mapping = aes(x = retweet, y = location))

#ploting retweets by datetime
#need to separate date from time
ggplot(data = blm2020) + 
  geom_point(mapping = aes(x = retweet, y = blm2020$datetime), color = "blue")
Error: geom_point requires the following missing aesthetics: y
Run `rlang::last_error()` to see where the error occurred.

Graphing users amount of followers and user amount friends

ggplot(data = blm2020) + 
  geom_point(mapping = aes(x = user_amount_followers, y = user_amount_friends), color = "blue")

Graphing users amount status by location To check how much users Tweet by Location

ggplot(data = blm2020) + 
  geom_point(mapping = aes(x = user_amount_status, y = location), color = "blue")

#Filter if parties are mentioned in 2020

state.name[grep ("VVD", "PvdA", "PVV", "CDA", "SP", "CU", "PvdD", state.name)]
character(0)

Visualizing the variables in the dataset 2021 ####needs amendments!!!!

#Making a ggplot
#ploting retweets by location
ggplot(data = blm2021) + 
  geom_point(mapping = aes(x = retweet, y = location))

#ploting retweets by datetime
#need to separate date from time
ggplot(data = blm2021) + 
  geom_point(mapping = aes(x = retweet, y = datetime), color = "blue")
Error in FUN(X[[i]], ...) : object 'datetime' not found

Graphing users amount of followers and user amount friends

ggplot(data = blm2021) + 
  geom_point(mapping = aes(x = user_amount_followers, y = user_amount_friends), color = "blue")

Graphing users amount status by location To check how much users Tweet by Location

```r
ggplot(data = blm2021) + 
  geom_point(mapping = aes(x = user_amount_status, y = location), color = \blue\)

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Sg0Q5ZZuFPSWgm5ict0lNnomzIQQJY3MTNstJ933i1tLtdx84RU9xdkucyJX41cvGtZnjZF/k52qBWo8FD3/DHLolQTUBaVH63ytLsLVdOgYjuorGWgwGmg7PjZmYKLuKIAKNdYfvahzfULaXR15/lRKKM22Vu5IRv3DIxeVUItL1SetBChkQtSTp2hqDQsgdGroa4B90Hfi9OgqCAUrw0rBkN42OFwODwrnVYsHXZb71EN8Ishx/C0NvFw5uqto/NP/jgx+r+73hIAonMEfM+HZbTtnSZFQKaQPC5fgJiM0HDcsk5jd6dJj2kfpqZeWw2AzoRtHzIkJgalXJZKnU6LQcjdzRtPOW3UiwL7MzcPAdCY9ZTe35MbHnJbPdmqOfZlMJiqn7gkjGODuI6772xY/YplXSM3nXxLncu64FXYQPXYXUeE97DuZ+ePtiuvRdU0qNgGxi/6AgFcDl/6Z5vdXtghNDtja3lm4wma+55z9rscjvRgxZugq1xjWfNy14B7zJFZ3u72V9In5/W1Ob0DzYSuVt5b4RB7p3RxvG9A2cb4kMMj5bQQ40MeTtt9Sk+7228d6e/nuTJKj9BIf84g4XL4wOb0+ovVNONDmWfqcPu/kV7i6w7PhBab2dYzdL6vefzrjn83Q1BiEkAaloCE+M7SwxaCYYgxMay8FAO2JvupfzFz6UeB+QUSUxMJTJ3tkZ9N5eU/WLweBsYmAGJw57JSpioeQdmqgLnBenQt+FoUIB5PpFs1WpIJ5q/45gGZ5czFXwaSCzG24pWwq2e7XiCmTBiNlE5soDHGfGl7iWJhM01D6XVwL67sVV5AtzrbzoP772c9/3sDW1UqUKJtKv12FxdVM0Kq2F0Uqt0ffCgZoZXnCY30X5xtnWl5RvnxpT93/Kej3gsDzVCGTzjGuv3PzBZWpKzq5RSu+JVvjgTfWm3/ENbP+AesI1/6ix8Qi+g+amtYDcz+ltUkJDSa4//L33R/3/HCnPXZb36r7/KzjiurBy0sNX687jc/XUhtKAXrDKyU80kAQBDQysVFJQSDXo4W51oy1qOLsFhB1IqikEoVnTOV5ODm9o7KONDAUuWg1IoQnN4fpn9FG9We2CjxQParndOIrWYbLJUZ2SHVJD2sHOm3pZ1sFVvLYbw3JpV7a6nZxdXLUofdfShV06Bi15C+T9uc3vcKYwCaT/Zw7mqor6+Z0NVRWs60zFy4+tKs79fd3voFnfY4vPTnjuHpdrvNbu987VtfGZlv913D+uufapYkhwTww/GBrjRrePN1ApLL4cv6TIRG+s/R0+rzSZDxpGg+2cO5i1/3SROAzmTbB6trRtvM7LUXvuWbU+wF1wLBOkzr1CSJkUxc9ziuAwR9LocPDZDY39T8+k/z/BE0IHZ+9eDPng9mD5CVKI1a5gUKuYGAva9TujRRzBgazCwuxLKdFLkuQGKZ1XS4hJ5kTPlGqEXOVrPOMob82I0K59i2j7h0IYrbi+W+bDxVd/vKirw5JZFng7CTP+HdVRJUiaK7ZjVx+feJT8MW7rh782J3ZdqdvMxdfP+lTHHXf7kqaVCxa8j6Jr6n0NzSGhgLQjPBWXqe6TrJhYu02GZmf9G60Kz9xdVrLJhofOe1xMmvHH7tH6duvDtjO/yBxYk16HR7H73gujJ53uGIAHR0YHR6/X3NoZEvp60wtFmYHu0ZdFoHf3DHIn3TIcXbLERk5tJ5k+KJwDw6EgtC0DcYIFO5MomY/FRD8sc5BiAA9cdbDzQeDEsTqyxOzS3SKDKfF8qQnHi+OLRzPs+n0GTQRKIIJGXp0gSAWIsQz9adZjG/FGWaJewzy3duZzM2J3OmiRxjyGKn+oMNkD9tReXC/JUVoHgTWpFE0TChpXLIZfWCrbSx+hN8ezrqTXdSoX9Z7LWaYU+nvWu4JzSxMvZuD6ojpAoVlWFtsWX8NFtbmmluaZV+bezh7yem7E98OPDGP7yKFk1ypamt63EbselX6Xk8lcCkZ+Kv/k9fgDAROk7bgclJfcDn+rLD4fKFYd9ptx3rXBjLyug/vHGHyFLYELefda6EmZuDKJ1ne806cy1GaghPTzdy3CoixPcpKRpTK/9fMFdfQUQGsfb6zYC0qtyRBdNxy3wKDnzeaclFNeY5MmaRaYhEk4Uui6m1HGMoj+Sd26mqqUBxtw0vLEp2qWo94nZ4NiZSJQvVlTcfbeSGtqmNf9uOfjtxfKuwUGl43kad7w5jUPFgQSUNKlRURvPJHmaDhK6OtnZ3AV3d9plV48Hgm4mWD7fY3norYatDq9HFE1jqdatzwdYWluLUGWBB+ykrei2Nkz9+rbPTIhIDwulpY5e++yvbE8dMrMQCUt0fndBxoA6uXwmbjzWC0Nh5Y/hvI81/8mcdq4CphgPtb72TQuaOkqJREJLI6A2Y7F9tE1MCkHyXCExOAYi1+jthDXD7e75wplxkDaKADEKmJESDFlGLQHHEgL5RkdmiIU9q7EOnzxSbEs3owWAGAW0meZMooqXj4ewAHeTJfrE4KCGTqiHbQ8gUt0wq4aHbR+Pp362ytIXB/vm64raVKgNzHiSUVULcw1SS28Me+WCqrp1bgkoaVLwvMD5UWGyyqPZkxRJFzS2t0th4cJa02trawix1pnhz24mTj8Xn9tfD4oHWybFxWvS6qPWD1tcCoBWxNSzfBiMkjQuJuhPadYR91GpobASIE5wLXJ6IEFkC6cVrcW7PM/Fa2PT48RTI8xNBYonAsGcSYkQSTE0tKdd+UwOmjE4+FiUiPT+dQqbwbp5anQ+QzH8KERF0IgjIqfQ//MUEqWSNEoqZhSAQm1cmrInmHaF3iMcybg6pBWIQXQCZhJwenkqRYDJXjyIO+aaDFGjEMqIpF86RRxR2dnTPX/qnKt0go9J/WSlu02/Ni/c+VE2r2Cp2GM34vqIdKmlQ8b5AxsYAEJqdsdvt+T/aNrBip+lEV7d95sKF1wWfyzE0Ds0ne2auTCFfGxlnKRGBBHzgSfvMbHg5uQ4Eb1CHFuqagosJdCKxJAc/+NhhETnKWpL5+fT0RosBEWsDNB6rB/TEV4lIL2b9+ETaOwCZRhN6LUYDehMHdYg1yCDosBoROd6hJHOWQUQQEYCGBp2iANAce8rGAQ1CCp2GRLqbLiWjO9gAIB+wiGjyrtuyTGN7nQjI8oF9gKk2+51QbFXQlN7oMzL0uHvwA+lvtQdAL0AyJZfeabd0xS2X01Ff0qg58vv7q5tPbFgqOeOje1jJfUu1BraRFiI7alfE2P0mC+9n1ciDlR9iJ0lHVNKg4n2B5pM9KGmYlCCI7pb8HzM5pYuLW1pbbDOzLw05PFIgEAjf6EhTjeaWukAwkmDS45KSluXXFjj4wce46fve9bVUMjjsmVhNABgPWhfiaDUkU9Q1hpP5DgXmp54ym1aJ6lMsrmE6/slGESFGQoNeh9lqVHqluHkTHUb2i8QSrEaJRQjMsbwOeo2cYDlBiuuTUTQ6ECCFrGgdFhfjMqQMx0+8/nKA20lkTZ0hRkJRPqTiGojfXATQ3FxI0diyL/9t3Z5aSYGgE27fASKKU6OpHgMFGaEFyjk9yBg0ANc9g79Nf2s6zKFs7mc5E3aR7V+MCkdZaWc5k5shD8m3f7Jc0rEsUmUDLfYIW2IMZT9vaVSRGCtlANVwgupl4d1PfvWA4p6/qB1yL5U0qHh/oLmlNV2PKjhLz8kuxVGB/CLZpcUtm0/2rPxgeMrp9fu9veb1yZswejXE+Nhkp93WZLParGZdeGUhTmDYJUWYmo5r2zuhoz2CIFD3wSdIpEnDpGfwGlhqregQhdrDsZdfXogQJgaRWJ6JIUkszsI7q5ltL4aJs0pgCRo6LaA1YVRO+Fgym1hJqIU4aLEU1o8SiF5XclGLkFyJH6AhoxjIWS6SxCH8Rn5e6EwERDwXWyHW8bF/9fGkvIE1oWBVTTRV5CSxcLO7KUIBG6gkhapOGbBVlI4WyzQe2Fpp7MrC+L5CKQPYrfvxTuTQPReiW8Wm3qNV+pY+QI+cD5U0qHifoEwQxNi4YqtQ0j+XK27ZvCZETO0nmyG41PB7nZMrrWmq8dZbgdZWbSD4+L9xfwyb0+t1KjWq4+Y/9Pu/8WdO60IcGg/pItx85KgeOk53mk3UGQ531JCS126sAhzpwH66ExFNfW+vFRN29+AxUUjf5k2dx3WAcOARrG1GtIsTt0S0Hxv878yg43inWQOgO2yx/cFHqKuFGg7+DogYuv1e56FaDbIenflTvTaMKdDrIrdZEUBIn1WiItjNxxuBjmdPaNJHmKZOFA5/2ECh4F+Hxg/kH3JaQIOtqamx6EXLSWRYz2sRCY9dR96QDZTxdBCNZT0ZdyjdSkeLncdL2iIlLZvOW4Uwvm+ZxL3FloTo3r3P6olL5Q2/BzjBplBJg4r3CcoFQcyGQldHyTk0lBa3bDQSkVwOh8MjBaQJbnJQGhvvGhhsDjIhBUD6q+9Ltp6Tzc19X7JjsWJK2y/aFx53D1hXZZO54WZNd6dt5dWbj7fz0LEboXUQMVqsFgNvB23S305Yf/94cu3v/j5IXbs0PPgmMrUNjWZoILQOgk5IkIwmEARSKValwcur6Gq4PrGQBIjfCAekCWujDClW5tCmSP7c4fK9u5ZEqLU22Sb/YRGtCLE4kEhCJglsipTuIYwLv5oH68Hpa5loy9RqSnPE3iAUnA6puOXm+e/lV4pKgCjzzuLcPPnQZEIgMByxHNYhALqzpy2FvwwB0GbPVG0JQRBSq3enbFR84nqJ8Il/bRd9GrZ3ld+edCwd9cBd4jfFLqpGsm/m/SDps9h5GK2a3EnF+wWKbqEbWk4CYG1hLBiEnmeyzvIlxS1pPIipzn3hz2b7L7acH+iC8aH+2VCIhWan93xfM+Nfc3gmp3NVOMzmv78a6js5O2NreSZ09YLukbrgtdVgIAwEAkAQaKpnLny4yxasWw3cThKUrgPLsDyFoJd1KdYW59dgYWIeIDa/etp/4AeOG5l/3cY4q/lmCM1T3h/oBxxBBAJzAKwBOp02Hl8M3gpgjLCyDkKtQV4ryhodv4W8H6IEpVxJTVkmUXt1WVZ4QcaJIRWeLLmBp1KkimV7EhlRS2qd6NvhG0rb2vAlxS1CxJAiinLlT2SPq2hBmMd2s0BpMKUoU3Ni65ibDdF1r7Oabk8ubpTfekvJIre37n2SX7Ia7CJL2Gq8zC6+qHv1wlVNg4r3DZQgiJmM12Nzm/kfPedfTzs0QFd3p/TNL74QCo30O76cKW7Z9YcdkcnvvxScpWV+yNE/ErK2cO3fu36w+NjJrFRpO+v3+/1uO2D/k6e1o1dfuuij5yRXR2nXBLF/ye+2Y96PCbv7bBskNNjdf9oCRz/r1EdoO2XHvL/eTKf9gBxLiVpMHccx0WZ5xASGj3aG/9Zx6FMWQGf3+v1n98mwfsxqc55tBxD101dD+/UA7YNOq1KIsr0jrhMAogtoewcPAdq1qIb2Dhq1iNCQiYgQ11PU0GlPa1u0ipFf+idBRBD02nUQPmBTDCH7eDhPJZA9a3MXDw3KoqnEBskZU0QNegrtHqVn9uZHoFDyAUhmGINAWYtHFqIx+9FgNZZ+X/tXO6mccv9f7vdob3dTp7KnqPwbrLzJjR4nf87tvaiiXd1D7YhKGlS8f2BtIRDIkgROdDYTMX4wq1qwPtqkW7jscvkCrNgzdb+7vnHaMj3skQK+4Rnnc33NzSd7VlaaWLisGDI8B0/bp6+kswGZ+Nnf+AIB3/CU87k+ro7SFn4Ny0Nd42NS5+P7gZnZQ0dNrGRLPjX2DdpN01ck66lPssKEtKLRGrU64GFbjOBCYE0g+ssJY4wV2yER4pLL4RgOg1AzHQz4hqcAUisBn+ulVcDwu9Oj1IsgMzVJJA6gqYVL3riYTj49Ncl8AtFiXEwJgMaMHIEEN6YUHQWJdZARTOgFZDmWkEF+K5AEAWGNd/KogAyClrZGUmRYQJJ44UmYJgdCngyPxihkFHK5E0/UbFSgOrN20Yfib4vrY+UgkMq6ma5Ojq4AACAASURBVBINrpb22IH6dXeP8vuff+wK7nmw4kZLb+/Nlz7ORn8Vu+VIsdXtlf1cPVTSoOL9g+a+8/58+8Onn3PaWM2k/wtdHa37nNfv9zptfKxboQyhkX7HJSWHo92tFJ5sbjkYmMto6u3ugS8MuFt9LofDIxEhsgCYLJ882Ry6OkpP32ePEr7k8EhMSHPUWQKziWc+Z9UlkDwuX4DX36R54Jt2E8EXLy/FgZhQk6ixn22dvBJIEI8nsH3VbmA1wcTwL/LTHefbGYSHnzJrlxPA2ou+m+0HFCutiEkDkFwj3BC+nUTU6rJD4uFVRbwnF5A1IimCwTiKjFfc/yMs58dXADKxJEI2blLZRoJpJQ1UhgUIgGg0Zrqlm+UKMrx8fGUqWUZVUZIkYlvY/JzUlW/ehgjficjfuZyoUiRkn6t0t3eHtez6KptOWDbgpXrisivakXuiKijSdmwPKmlQ8T5GXvaGgnwNrIz1O4bGx4dcoz1ev9/v9w+e+EdP/0sA42MTNqfSmA7N7BrwOm1gwur0+v3uzsjC6C9CV0fpMV703Dpx1u9327EQ59iX/ANdwWvBiLHX6/c6raxcGhqnuftj2JrA0mjW6Y5YhZXrY5Om+joBQRTF6QkOYjUhFtn5xeO9NuynjyMg3np5QXFQ1DWYtZJYD8A6EUXqa2hYFJKQSsQB6veJoM+T/bqDGmBfe70o5KVgEMhllRBESBetkPM9IUsgpi/5q0qaCqE0c3Q5bMHPj8IclzuHoN/WqK2HFN4rlJWFill9I8l6lxXgu75KlRPuPAXk/aMdqZ4dlv28VaikQcX7GTnWUMAZqPtgj21m1jrgV7QLhKaDOg42AnRlG2luac1NZe/3f6uvGayPWuM3X7k4S4/x15L9K4Ofhq5ue9hsBmD8h5OYP3GqGdDS2iGNjdPV3RlY5Pix/QvxmtBv43LouuUR7YqMnDpit0/MrKDTkJKzl2yrVQOp65cDSJdCOplkgrRgTyzqHuJgX682d4yYTUkWkXPWgaU76wKpRObU0JkT7yj2AiXHZPqWLSvhkel0DSmNVqScHUHQky91UwKiSF3GSVMu9ZIsZ3OwNJT5vWRXyPtssFXmINs4BuVYg6W0de0+LT1RQcxXvliX6g9UbIS98LHY+ZxlZ7hXxEUlDSre18iwhkLOAI0ZNqEkiXT55iIoqZazWSOVyEwIjfR7XrmJ5MnkkdTSMCNJQnx1RvnW0T/7jLt5AcnjcHhutiv+EC5fgMlJfjYWoutP7TquSwGIRiERI3ztFgCBKxKLCd5ZQr+PE3Yb0HjkVjB33Z6PAx2n7YgpQCY8hTR8OU8dsCA2IkJ9Paamp5T9y7JiZagBiDcrjoCrUwvKDPHsUAFRTPsiJBMpmTSHEA/kEjTLMRT3DLGWA1qMIqkUK9EKMinPxqKpVz6HK+VkzD8Qo4H1Mj1yE27r8FwMl7Zt1wayu8K4SE5UsIvv0QbuK9xN9469EMaV83ZUs9ZGu7r7ji8qaVDx/oaSvqGYMyjt//WcwzHW7fd7nTbrCSW8YHzI8zOwu/1ep41Ou03pHJwuDNds/++dNllnbIX5q6MBArNB6wkbnXabzflZK/bTdqwnrDan1+s8KF0ch5YGTPU2bE8d09HYiB40tYeU5JRPN6CrUdQJsI/5t9PeDHoTVpMOYPKSlFXbazGiO/ZUgwb0+wCW5hFNxBfRrv3jCrDvSVvayqBoASYXNjptZFKptC9CfpfUck0mLVQGAtoEtxOsJjMjQah9SJfXQchWsMw2JkuLPmwCsZyzwR7cs9Y2+X4nAQJbHXj3r5IV0k5XwLZFVzWr3CcJoHYd2/79lrUu3bU3o5IGFe9zNJ/smRm7OFvEGaD55GOJEJ3dXYSujvLEES0zs6HxMcmsxd7dRXPfGfvEW6vMzIaBtqL6yo0nexhd/aBd+s+vJJxOu/TDf+8LcPNX9Jz8dLdd+lvJ+knHJ/mv51yjrZ3Sfxvy+IJElgIEXl7usD6sIwbJtXffCI4POTyTCWoESLIQDHAHUUO9GVsTxijv0HNcBwINR2izUCsg1pBsOvr6y4tJiN3B1GQ/CvEIEVi4EwW480pAue/LICDWgaxFl7ZKCAhaffpTfV78QuaAUVwrF0mlIybaO9L1NvMTLQjoDDrktVtZpYUg509SDMvpz2uLmkwd2H+/NBYyFStp2imOuv2/X3rWlg+43HlI4V1D9UGD2YfayPuhNBCgrL9k2c9VYte50c6F8f2MnWxvV161ShpUvN/RfLJnRpoy/72roLhhaOTcqzazecrjcLh8gcCrS602pXbFgmJncDg8EnNGa2D23cLpwpMhJTgi4BuWCE8HfT6JyZsdNlbnWluaQ7MzJAn6Bn2BuQABaYKp19YO6zA32pxe/7c6dZN3rA0CwITH87rzSx1aENDArdvUi6wnWVogMMeKTCry8vU4yKy9zXSYNZlUShNvesbbaxRA3+39HD97S4eYVbcLcKCBrNSQSa0ACeJpq4SMnIilPy0p8QsCxhwfKnRDTDE1WSblsqyRo4V2BLlEdAoCmaTVhC99r9i9MjKJ9JPSWMiq/CoBwWDcIACiBG95HD8pOUET26xyuaf8oBrNtpzJAplFBd6wvVQBm85cYex9Lo/fe4aePXrnKmlQ8aAjNNLvKJL3/enUTNWhue+8/1tf/mSmdnZz33n/gPXqKJ90nGp43O112mxO77cGBs57Wy54Xsfu9mfg/Rxa5zOPAUe/5B/oUjZi6WjG7vb73XbsTqctbYswnuixh+3dXTSf7GnSYG2is9OMye71OhvqjtY0O//keGA2yPivp83aoOmjVpvT67ZzrMUCNJh5rMfW0NMZU4paYeuwEF/Hdtbv935cC7kbeDLJpMd7PYFIQ3OzsS4irpMyPNVrs1o0CDIYnx1sEzf0GSyXaGl1pbhNNOSZ/TNyR5uNtpANRhGNgO74IxutIMskNxFYppK9aFNVnn5ydDW+ea9yUFjJvk0KVlU4i/eaN5SV9JsygHuusd+5KqLs52rWrew6uulmtip37yE3KiXmZT/vHGoaaRUPOppP9thG8xL/hq6O0vPcVnP6NZ/s4dzVUF86WuLqKD3PnTjJdy9+ZyYUCCh5ojvtNhnpwgv1F36x1LoiSXP6A87/q5mrZSfs6rZ7PD6bc7DFdmml1ebzSaYmhvo9UsBqEIPzKVFYICK5XOish+oWQxPXTdfGHdIqwMJCkGsuD1jeckkBgMBcwEQgqkO7Xt++LzwZBggMOxz2Zy3CT+eaBvuSg5cyRSDenE4YNETHHcNvAxB5+bKiENBqaFj0/XNKSBeoTOeINmhIJpHzMyIJaPTI0YJkCZqMqiGVlyQim0Q3lxQ6dWd1f70QX0oaDHqBmIiYQpSzyowqUZoPOrHbAZelqKLeRdFZXFaEZ/vsRELvyiRZ7DWb2d3dll1iq9joN1VqW6nSl6Iax44t9d8edvK2d+cvU74nzFOFil1EaKT/HM9loiNzP4RG+l2+AJB1UwyN9J+jp9Xnk/Ia060Hfb4JwOb0njd+0zE8Ddh6O29efu2RwZHBE+NDSqwETe3MTQFYT1jjC8G5AIDORFwRzJZ2wlMApgMntLevLQBgypRO7HC7D3qUeRToTA26yGKhkl8LCf2x47HXr6cbBHSyVUcQM4kFRa+QPZO0JjERKZsIaf/J+qWrSyBi/3Lv+H+4vFpfx2KJ2iAfun2Yo4QTlC3/oN1PYrnCaINAVM7bmaBr2JdYvFOugnbRiSpWJa7vGvb5/d9Lf3ziiSeyza+++iolIqf643NL5/W2V9nGWluaYXsz7/Bxqpm5bFhB2UVLr92lX5UaesouUeWK9wSlm9mt7anmCRUPPsrnaMpLzZTOwgRAwDfa4lWKRUgXMlaMRMDnWz3rtNl6e/G5HH+3YuvttdG2ermu3/vswuDQXw95Zpxn7XR2mhenjttt2N3eJwJzcwfdStUJXcTq9HqdNliYsiqh/5EMYzhyhIi51+t1WjFN/pVnprfXiqgo+Qyk4skICIe/6j6OgLleABJA7J+vi2CyYkQUZPRHgg9biCwQExB0mDWZf/H6BOsIgIAhU04CEGS0mqtLyg/7pb/5f1eBxdh+dLmjw5Bf9QqA+B3Cin+BMrvid6BRCkqQWFbSQUD5nAjRjKcEgAbk+OKywhhMh9KK/3In/MPtVTOGaj0VNoKhOi1tla4TW8Fdtpdv22VhU5/8nXuD7gVjYJfe6l6Eq9wrg8Xe2aRU0qDiPYCyOZo2zMJ0Rmm1tqRdGwHMZg42nuzhFzxms56IG3se3w/Gg8zMhllBuiQR8A1L1J14usF0fWoV5q/8w0q88w+7lIUuKImdoOMRdGB3D57QYTntddro2P+22bywFAQtBxrClidO7deiBbnxEDI1ckTUahs6PzwfQWYhF4YoIzQQCbJKSoY7bzMVxqgDGTnOQsr6KSuCSP06UUVOy0QLQwUTGWtFapmFBDKQXCaePkB0D5ujhSUvy1gqkzICJDMFJQzIGfFe9hAS8r4rsCSsrbVrNxz2zlSZxvJyu5ynwqZG6bzP0ZL1y5Sr2oSbVBkieJ87/ZVir3e7c3m81bdaZVKEXfQF2dQpdRsvucJTb/pC9ihkV/VpUPFeQMYj4WSRP0PWqAD27qpm+c5sYiXA01+2MAqw8INBX57xQBr2AZp1Dv/61YTAhMfhUKwc5BYCpj2DtRrSWZBuKgEXLuUnw09cPiWh0B2ZFJoUayQWLruGM2NNDVb9neB8imRh1qP2Dm6FHjKkbt1OgRx8NaEVdQkhQQp0IvGNb+saoYzXYfydheznepGl1AYOA/kDi+pql5gwNjycUktT+dsrZ/somjklU6tlrWLW6k0Wrer7MuWqYG5onEq+kBtps4sa2YFhu8huXUGjXoq9di+4y+uWdWOscomNulX/9nY9wnZLfxIV/pa28We2kTPEVn9xqqZBxXsCZXI0jQ8pqZn8Sg2qKme5MbOY4IlszgZBi62Jjo608oC2s16n9cg+bv78dkSm0+33e52/8qRzQPWawUCbBbv72dokK8sAkytYm9JFsS3tnPzS4AkdplpIzLFOTQMiYMCEBWuLBiKLwXlFxJotoAUQMKG5NWl7/KFbt9epNaAVWQwnkmssJtFCfF9D5katU+7oMmB53ARQwBi0xSkRAJYqmAcqXI3yTxihvqHypT//O8UXI/uNvmSgDI2fPrVTY8S2Yd0kekJBWd14lakRqkH2mrjRLbP6Xe0EVe52j+wvlafadQ383bQi7YU6qvKcm/7FVrkZlTSoeG+gJEdTaHYGe3fu+E8HVG42yxOLkbjt8ayuouETn1qcY3ISyTM0pwPj/EXf8jt3uB1dtXRYJ8YuDPb7lnWYfu4ZGg+/vYJhnekwP3v10VYd/NT1yir2P+oMLrLCoQYdS1Mm6buvRAE9ellGZmWRlAbrfj7m/tKR4Gy6yFQtWtC2fvuUKQEgEyG5Yn5MdwNk1qIk8sR8XIbFxYwIT2scBAHCr6VVJFoOaNJ+D4kq7u4K8m0NlTqkuy0tbtRXrENALBMLkRkaKzdwdf9vo6Wt5abfOIB0mxC2xVY2PXB3XVe8u/Km7Mbufy/5PdLA7xaqjMvYFdw1xqOSBhXvETSf7JmRZnJ5HZv7nnPOeJQaEedmW/P8FyrOcuppW3NHXhWj/U9+wgwdbrd9KRiByUszzv/ptAXDYcLXgkgvXosTjbOyiOQZvBYnIdRjIPLz0ZzIe+SEJc6EZ/BanCjHzn5y8vU4kUUa2uvRYtJCkuAcksfzdmZEco0EJH7uuBIBEBqsWIgvXL5SGPugPbDhY8gyWlNjOtdBgtspNGKBVD/w7LNl7fm5GYp+FnJekLkOFa2bgqVJBFIryFsPjlh78ZfVjUmldj2T9LZKX1LRLX+PUI1sqP5Gu3e7vU+cPO6HbWzpJVfpinG37VBqyKUKFTA+5LjQ4s04TpIXuRl64Yuuy81u/zesI/2u2TPpAhPjQ46xbv9AV+iFL7p+8xX/N5TIza+43vii99ELrtlT3g/+2jUsAWjhSbe/eyzt89DhHtR9czD0Ge+pVwd/MBOOyNjd3pYLrh8GWMpt55BZeHehJi8S0bC/vmY5miCZ4EPPfvw3L/40CmDUUZzHqF7LcgI49LD23RuFeoX6RlK3yyRAUJD1NCjrA6FASdWg3U9yuURU70O8U44ciGjWN03lVDykLMvY1Bdit2A57f/2F4ByIZcFGyrHEvbCtF/Z2W2jztuLstt5DoCi4TuJ9Cv74Nt7sVVuY3fDJsvOtkfkctOdb/oLUn0aVLyHkJ/1Mf9zruRkfk7IbGvZfMDZb7NpIxV3yYDP9bVM/9DIOd/NgM/lcDg81/NE780fpseOPZQO2NzfwE1PeqblFSY8Ll8A6ZVfrM5ga8Ks5cBp/wBDnpneXhv2s84Vzw8WIfnPHl8gHJEBezff8QVYAtOxhxDQYMHWUVejGAD2HbPS0Y4YXY6liCVIwWSaMWA+sPp7Zz+VC5wUQUCW0QrIFDMGYGk+zRgEIaMjyD9GMjWlkjKajz5dTkevV0bJcaNcehsvyxiAVI4xCIZ0WITOur/SdS9VvDUFd+t6c2TwC9V13EilX1ZhvpNrbnbOKv3ddqKxrzA8/xFKH2ejjA67iPvZErFt7NYTbap7qBBOsqUXq5IGFQ8uxoc8uEsSMYwPeWaUCpF5jRmERvozY9ytvnMjobz+7o7JtDnD4boicNDp9fv9/i8+FIFJz9B4eDLIZDAz8yVOt/pcDpcvwIr9ub5mxodcv3ja67YDMDn8/QA3brKQIHzJ4fBIBC5fDvCzX588Y78e1zA/GbQ0YdsHSB7PhLK3yG9u7ashaQx31P04mIIk2o/82ye0LN8kBdHCck2iwMJtpOEf5+wgKTQHWE6ihEga6tJytzR8UZYtJ2xoBYDO052YEJSjg7SsTv78R/HMWE3u8I8payWjq9nM1VWmPkivm0kxmeB3s0klTPbDZee4a8KhZPWq/T6o4qTeNPPBVnGvpOZGj3DPjQ7vPWybX24q/nfFBUQlDSoeaOTXi1CcHsfHJJvi2dDcd8YujRWwhtDV0UDGOzKdx2F8TEpnbuj6htuOTeEPx6Yz6Ry6/tRpw+4e6LLU6zF9NDfzrW6v00abhY91N2en6xrwOm10dPCk+2yzTD02p9drN6WXj0guj8SaHt2Jr56qg1o6OrA5B9Md4txJwSorJwZ7zYDuI//yREsrb+uVqtpev9dpw2KBIx3oRAyFkwPJO52f2p+WgtEYegFKNf0C+u5v/2/PfRwZQc/EpQki1Aigo5ZiWZ0StEKmRZ/x9Mg/zfJTH+TYiYBOiyAggKDngBadgJh/KQ1K8cyPEelG0Rmmb29kg/JUpeeoIFTrCKk7ailpEyjDTt79v69VN6OC/DCHbfCDHRra73J8wV1Ghf3vVtqG7XXbyaKVl6j893MX3DI2XULN06DiwUXXgHe2XykLgc2Z80gI+NKNgK0lV5Ui3WDNnyM0OwPpFA6hX73GzYLG0Ei/a/YJe7Z3RHI5pOxE4ZZ0AqhrIxe+m8tODYD9M4cQWUoFfC5Xdmmn97zxnGM4AAvPX7GkUwsFfIOB/B0dcZ/vs4yM0q6NXxt0KPJrYjlslHIPZZlnLak8qCt/KImJl1NotCQSkGS96B++xekdfOPfuCZiYw7HGABRaHAOOv5u0PcuCQqzQylOBLl4i1iY+n0s3clYZkHWoEnmEjykyPgdyMRzw7hNGVTwcohNzQOpctaO0kGyXC1piL8VLm4SGpAXSjq2fPZEdTMWTLWt9Ay7ktShMu4mVyjr0rFDP4+N9Df5n3eYtmEb3TZ6orKPv+2dbLQoe/bXUs0SqqZBxYOM5r7z/gJbA0B+IUp/nm+jgoIoitDIOV8gXera4XBdXiAieUZCBRkkV27NZD8rSR7tbr/f7+0ZHXxllYMf6gz4Bq+sBnw/kLC7vc5GoL4RoEGk/gh29+AJDTQ4vf7nZgccwwEBq8XWaQmHCcwxOZmZ2tzr9XbqgLc9/S8BmJtMmOyDThsYhbVVJQ+RzTnotBB+x3qiMe+Z9E95/enqlfI6iQR6EV3ehUBr2mcAwj6XayIJOi3aI8+2KVf5Rd+gL13dWyNiMgIIStXKkhNjOUpDvgLA9OHcIkLhtV1XX/WFJKOD2FKoY77eYivjCiAvUMZ88/Z/215p7GoXvRc3/p3fUCtYxIu6lSrA7/PAyK2iGkvNe9hqo5IGFQ8CmltaM5aI0NXR9MV8fMhRpgR2V3e2pkRph+aTPbaMxSI00u+42HLe77Zj6+21YXeftUHb2W/1NedPwltSAOZH+j1LEL7k8gWQxv56yOGZTADc/OcJIByGCEge1+hHrSJL80iewTkdS2/zM8/wuwLGhM/lcElR0IvcrktMHP2IMSeuDKf8p3WXXa6JOGAkMOzyBUyrBw+CNOgLwGr2uA34Bn+UxHoicS2muEQ0thkh9rLLMbgkUiOCiAhagXjGuQFIRO5Ehdxy8QTJuReni67ycn3P76LVYxKQISEjGmhAo80T/vI6i9q0g6cMLP4q51FRVJgqvlSpJqWQzxBSssIB4kVdMpmqyowuYApaU/HX+8qFk9aWaQMM6yVNv32zfNcNUWVOp90SnFt1OdwtYZb/CHvNA+6H8Mh7gvvfoqSGXKp4MJCtWGlzOlt9s93+ga7CLNFufzaRX7Y532aRRem3uZYmWr/sf2a23zV7xttyQVlQa7Emjv2xc2bYF6CxnXmlhqWVSGHeBwPmWhZyyu72TvPURLPbe+LC//o3gcVCL8Z8NKKfT/sVimAwIXxs8HMz3xoNzAXAAmEAvUAsF4Ogk6PxNgtyjMD+Ot4uqVypTTvzmRpgacMYy3wcOqx990ai7Unr9Cvpp7LUc2s5r1h2BvtOPytfenHt6MfFt36a0FETh0f9X44rVUHR1rK+ViaMQkNxmmqtCWMkP8oU6j5/Iva9a/n9qgmyFBFTu1sk03jW/+KnlY+VQy4V7DCCbnuq+y1FBm41jHAvoka3hGo2fA83uRfBpVtdetuTbzpDhYiYdAeVNKhQQWik3/VGq3VCKkoAtb+W5bUOt/8b1pGvuC7e4qFnBj85OvjKatpQu3ibhv/B++QV1yurHP2Sf4Ahx3nJEKX+Kafxyhsm08TcH3lP/th1eQFDt//iX4aGzriut9rmD57xD/A1h+em0/ulay5P1kJRW4+4ROsp2+SVQPEOFZg0RNJS1YRNQ2ARoQ45SxsETPtpbWKyqAqUgEnMjBQQSspVGxqI5pW6EHT8wV989dffeL7AR7EOzQqNZweb/+PgLxPpGANRINXoPi16RsK5OQVAh83MO3OsZVtFELB89ulbl35UVsYbugfbJgbflKmpI76MToT1nLJkc+jKV7TaBsy93u/+62a4K6Rho9m2ygMqDNxThrEX2Ls97BbVqN6n4b5ChRdbNr2H6tOgQkUF1HW7nTbsbiUmwub0+v3uRw1mLJNj47C8guZ3OoO+l2YBaP2y93ic25iPP5abYXxMajpINEn4ii/AxC8jrNwqcL5rMTIfVP5BvjkLYV+OMVh7e237NEQgFldCONwdYDt71gYCJj2mfZhoOGLE1Nlrs/V2QuAOgJzC7vb7T6V19PE4k0u9vUWBAnKWa2TsCA+ftZsQBfToIM0YdB/tNYNeoCaB5Hk+qsG0rz6n1V9Bo+HOr28hcODIPqUtJaOPffPHJ+1lPRgS+WdOCpKEs4yhwel12zFhJO2gEB0bfD1JMkV8GSCeIi6DgE4sV/RS1Bc3lmUMGs02FNxrc8XaqfIoqz/fFcZAOS6y0SqyvMnA+1/jfXewu4GvZa0zD6j3xpbejKppUKEiHSVxxt895rjQMtgzOnitjoOf8XePOb7/+qHo7XdTvd5jf+t6/Y+8p/7B5ZvLDNJiPYBWtpoTwWuKWSJjTciDtc0UnF4BGbvby3mXlKmZaYCHz55l+NuBxkbmM5Nm9Pg2p/fUKy5F558Ps5kGrTWxFArG0jEMBrmtnumCZQ1wuJHp+eLB5TMtNhrE+ViVyZiL7AWl5oOGfSzeKR6loz7BUrFyw+m9+JuvOsaqtywIIIokNx5gMBAtqVhRjYmjcMA+57e+lzZqlWoaqpfl28C2b9i7mPnxPaxp2J7G5b0kIas3rCgtqqZBhYrK6Oq2B3wvzcLBD9mlsZFfvWaaNn78QyZWbl15K8LBR6CONgttFmxNtB3g8DFrYDV4bYVOt9dpoz6M7ZQdE1Z7JzZ7Jzan94+JgNaA1fkM35HiNAL203asxxoJ/KfL7wgpPvAJOzan19uph6QBnbnX6271ub4/DR1ur9OKET3owNw7+HQDxILBmNx21tupBYhqp4+etitVI2uP1gFEyTEGIZ2tAdAZMWtyt/Y2i1JlYT6aKmYC6Aou97kTxfz5jvwvlGGGzqPZllLGAMRZUnwd82tsRn2urTAGZbUKjAHyGUMudmTLR768trzRVxUYw07ul3ff6a/CzXIjtcT7yjPxvRoHUb3OqcLfs0oaVKjIoWvA70YKIF2SkHyXVx4ZPH+quQ6ky9MmDpZ0r+t224E4Ex7XaM/gZ5oIvNZytpWgNEFAmup9rq/5MVst1Jwc/OSoyzNBHE2HDR7ptgcXjncSeTcqw8QliYDP5ZqIAVFq1y+7PBK6lKWWX3lcviCrxCAOC5cHfQECS5j3Mf2fvzYha5CprWPikkQCNBz6xF98pMBOUN/5B9aYBsCgIX6HhWRa19B42vuktiSRA6BI2XhdrYAopuMXlMNDAG5/bzKVTiWZQ3Tirbyf7Kc7S+cUlHpX+bkWDabtWA7S09FYrsZ3HuKVYjcK8EjJTEf/dXXWid3BrsikHRogSnlDkczYIyG6ERHZI3vKA2qm2V26tlFMbNnPZaGSBhUqCpDOAOl12rA9++UTgJY6sPfnojOOfsnbU8dDx2ySG9DMogAAIABJREFUxzVjpNNuA1pbLNTRcdA3nHFTWLjscjhcLyfhd7pP9D3ntNFpt4UnA/buLmtL003phtOZJ1873G47ZjO2z/wffr/f7/69yLEv2uvSX2oEpUe6/8IdCC9gEBCQsw6MSQK+wV8mQajHftZpw8TShBRURHW0QIrq5i+5LoVAg8GCWYS6Q/lfCw8djMikUplK20DelV3xVNCCqZzk/vn4hKmcFJCLxHg0UlUVK/3BslUo5svkejZWrtu5Ad4smem3f/7SFue4H5Iy7dCafvfv05v6YaSTiO2qeuOB8zkoekt7p+yp/s2opEGFioI01Hkt2XDNY+58xpD+XNd93u/39tQpH9KNBz/j9/v9bnsuwZT3X9k4iJJqItszODt30Onu6/tTp402i/WEFftnuoCGp73n+xjpd3x9bMbWcqjlYJq+7KsB4+RfKVUqTNiaaLNQG03oROpPuzvAZAAdaWfImiWkYV8A9FCf3rZGQDRwQIsI4kPNgGCzJhJEwyykYOXd/IeX8/JZFcBwvDNTICsBkXJVGhbfJinnpZoVTdk9FCBz9ClnYFHSaIOSghqI3azawJDOf8X2i1uncbRxgy/uwlX17ki1Tc35m8qnu5PPuOznu4D7XCdxby0mKmlQoaIsQiP9SprIvJSRX/tRgJlvly+emY+ubrt04YUX+h1D4+MXfYG8xnTWqWx9DODoZ58IzKFETQZ8f6GsOCEFAr5BXwDJk84ixao5Dnb34Ik4N2MYD7JWg0Eg+c8XJjvaI0mIQ++nzcA6ltNuO4f2J1EyIZho0MqsZ25uKe3Beg2rN4KWPFWBAKIOOh4x5DdlSkgYNYoIvz4Ryx5ZOTlvLDzEovlZGVJ52Rjyu8npaZQzuShpdFRWslFvExtnxSiGvvur+4rb6gvpYyGyQn0XMyaV/bzXqCZMP/vjloI1tof7zXXggdNJ7BY2pYMqaVChogjLi0gehysj7HPQLZg7qpkgNHJB6mz9xeUAkucCdlu6uWvA3epzORwOhwd3XtKpxlNPN6MYImz2jym9daa0jsHudncoov3IV77rd+MZvBYnssTkJMRZTjA/GWByvk0AiFx+aQFMHZ3hWwy4j9zISOIIpkYd8jq/8+RDSRnemVhKQpSwQkcEGk91aiEVh8kZvQ4sHQYQzCd0CWQZGdaSyKBI8sxBmp7d9KGHVvOOVlPH8WyVbUDUgul302aW/ONGKOEEAm2/W8nCoBM3KTNhOrJBCsmNERt7vpzr5t1FWR+Cov/2aN2yn4uQ5Q27K0Q3zSeh4u6jGjqohlyqULEBlDhMb8sF1+wZ/0BX3o+tdkmSAOzubOLIfNjOenuyGZ+6yE9dmZ+5snAhf/eY41uvZZJKGrD/W2/LBdfoiq3nX2h9vrzMxkfauT3VbmRqDsBwxBYVF5hZFKDpw5bwZNjwqD36cLcbTyZbJpbT3pM/dr2U4JFWpdqF1WYKBpK01Vu5E5zOqvUN7Zx8qve14ZcSrEcy2gIBsQbNei4lZTEyQY06PakYNY0k8oI9BZGao08I069u6Jmo3Vox6ruA3O+oQnKn7YUFVhPIV01S6r1DhdX3YmOVX+N7Mu5xSyj7zvfubVSVi1MlDSre38jmp86Dzel9jnOK1C8kDa2dkjQBimBhyPHdJWuw/ouKdGdxgYUPnnWuDI+uAAgCsszqXDp3g93tH7Aq9EARSaGRfte1unTNKkO3/+nfOn7Y/NVnFy5PLmYSP5iwGQnUPXVs9uXXNR2d0cWkNTgVRLkci7BuoClK2ARK+of6oyy9BSDqqImTQGtAH7UJ6URQhShM22A1EloVdfWpWNaasL8DeZI7iCI16yCS2DgmQWhsrZmfSW2UFaE0j/Tu46iJtyKbdwPxAy3Cb2fL7Ce9+6pIA1sXaZueyHuaBKJ6bOnGf69SU9wTbInz7Xp6ibvwcqr5dailsVXcW1SQ2VnpuoWZWvMv8qF8EV22/+wZ/0Dfc87RfI1C5ttyY6SZw702ls648Xx3cCq4aEGOF/r5TQ6/YgFIrBIslGCS5wze/1kQfR6HBHTarRPvQL3T62/7dt/g9bEzl9K04vlri2g0NH72bN2l4VCEWvHl11OQnHzdTMMidwSQER/9dM0bL6WihBGI1KTl/9J8vchyClJxWQBIREkQAJOeSFJgPZtAWoD1tJRURGVwFUFIxZaaTMylN35n0thcu3ZnDSVHwsZSX6vhwBP7b10BOeO4kJlWAFlPm4bp6klD+SRUlE2AnYe3i0t7b4TUb2fLf5Ge+/U3oZo/vN09xPdOOb9VqZMNW9i0z86xadLie4uyyZXJXAgq9K/QZ6u4y54um/46VNKg4t6iue+8v48SAV9eZm8OyTPUvSWqURErSB4XwA/HB7qsgP3MqaXzlyWPBzDdoclQPMJy+iyXhlebKBeNGJFcg9kf0nUuor5zX2JJpm5fZElS6AT/P3tvH9zWfd57fg4PAEIgJJKiRdgmJAtM5SQqq6RqTEYcs+nJy7r13sCql/L4qlNkmk1miMY7XM6d9XaDzFzuTJG9484sVzNuoN1k2w0y0boWmyjIvd7rNjdoowxVMonaKIxvYjUELYK2QYoiKYEkiBee/eMcAAfAAQiAIEU55zseGTzn93YOyN/z/J6X74MqoR9GZDOTlaBiR8vyUhQEE3KazM0fZ1ShL2tkbHw1owrXwpN9vMXBsqY8hCrPRYR0/qIsA0KS7JFbZj26IcBWBlko0DBAKTuh/pxKE3v9Rn5kzf9lYNM0U0HjKOVszGAyky71WWg0hkcdvF1Mvinn3kI5raO66WuIo9w7NCSXsiYxViQ/dk+n2UuhWJOCUmu853sgJqOa92MEQhrYv5gYU5IWtOkKkzrX8pB8Pvz6yQ1FHSfH1LQItfXEX6tZEpreM9mjq3VaTXuYffnfhTcAujtIrpItFBWbnieywHKK2CRPOojdZS1Be1HJZjjRoxPKF1ngTorVrP/ADJbeTxyGxPxrMwIWOweUPpnVmJ0DArKICcgsiALNH+jVpTGQUU/8pmxo4J0FWpSIOoujJWsMIF16cI8lKZCiahZDNpkhfydT9HMWgvqP0PlodmlpHRleYYc1m+XSetWFKNEYwPTbn8zW266VaLIQtv/tj2vpX8NMtSRK5AIP72MMv3b2fZ6FWA12NYdzX2kM9cXPVvl+DKXBwL5FePZoQGE9yKYqMjnmn/UEQqFQwDOrrxz0jeiqDSUd+0aU3ATVLKH4HSSfZq51aKLXF/A42SJyPQqwvrmOCckXePIQ7VbWNgEWvzMa7aK9nXYz9uWL34rhgAQsb+LspN2EAM0WzDidm8UiyvJkQLJj11j80rBl+qBVIP7jKdIk47lKj2nibIHZpOoJWzKbv/qJdsTmY1hyf+wyaHmXZbZsyDIkY2uVs/Q1/xbj2Ee2z0+QAZoE5IW3K83TlMvT1LQ69hETkEpldLkqKyP9z9+rvcql5HuhJGFDl1aiBNVnN2jb6HIWlY5QK39zUZsKXUovVi9gqtFgdF9LTcursmWFBruUclKad1rl91JuYUUvqjRlpvIU5RrUYSDRnaVcX0NpMLBvIZ1X0hKdR12RuSho+Q26Bs9L4Qldk0LfiI+s4M9i247S+acOFc41t0ZzMy2AmXYri7n0hU3Cfm8wQixBfIXIAtPTLERYXmY5RTxOysKamVQGyxaxBRIyFpHNJCmi4WzFaps0erYdIPmTv725oZSgtJ99vgPl/D/x8koTmax9XpH75kccQBJSKQChGRnkVAZAUGtFbN5SNQxByNsYAARE2FjTXkAhWtCzU+T/LcatH2sP8QJg1VcvMtueRTN6qsutbJLFdmfZSnthLcIi7H95vfja1vYs0tXvsKU7eKkUKUKt/M0VBtT1R5Qbs0h01YFyGkmFHyssbNtn121Q7uLOdQjlZeqydGyrOpQurMKzlxuh7u7Vj1wNDKXBwIOFiMJ04Hb7w8zOzeu26Xtu4OpLRWpDVR2LsBnP6wdrt2N3V4gVF1F0nnZg0V6QMR+g00ZLExkzR9vYyLCZAbD2BzydACYbjp+PXlsWAcH8vagqh+NXXsmmKpro7sYsgxWHFQEEK09+9qvn2kCm5WFSIG9C7k9eThZZ5GVZtTEIzl4LIJNB9VmI+XISGcopB7oQ4BFL8bXWj5azPVgdNYnv2lBp2TXazosXafrd7TrUcuivqe8uoXpvQq2H1H2FffXOdw+1Pk6pJWOHMAIhDTxY0OM5KEHX4IsDwy+NHx2o1FFHb1ib8AcjkI9HBFyewGemh0fnHzr92a8HDn3OGxTOnrVdWT0/NOu/KAz5el73Rq0s/Iaaabm4SHyVuJK1mGEmp2QIJCa8QRAgvU5kHUVmNz06+qHE6M9SakEHBRYrNJMCEtlS2wnCfre6yFv5lkXiwHL6hff/7OWfaUII5eiUYrMXBEQz6WQx96ISzlgNhCbkdwocADJw7/jj4j+9kUEQNYGIypJLww70INKswwEhYIIKxSlKoxfrgsknpf3hootli1xuj4YIXV3zwHseDRHtjdUPdjX8c4+xrf2pehiWBgMPELRMzGPu4WJjggZdgy8OXPXncjmr7NjS78tVq+pug05PIHRhsOsXkSRriihpoefIlSsRwv6LK+1qkYJUBge/DEaILBAHTPT6Rk9rqkubrSDTcQKyx31nJw4AMr8YnU5CEwh0ZAMnk3GVvCGPI6O58pXqsaHrhUcFZQpzx9lTSrJG8vqVVc3B3ySAgK2NwyKyTDoJ0KIaC2ytZoCMjOVQwVQCmPQOE7JeeKG4Hl4Ryt6tBhmTHmuUXEljIKcxCHpVIoRzoUBbdZt9qcYAto/oNNwW1ezCum0qd6wm/LDCCLlb5cZprHZSOlpRDETpj3WPXE33at5M9SNU+LztaEVyurRL9b8G9TVoIAylwcCDBA0T86znxcFKzueuwRc9rkodu84MuPLZEyU1qx77Qsj3m0qXV2jnieSYUhVimiFFsfh0G4vf8QYjLKeITSvCp8Mug0gLIJMR6Dg3etpCOoHJwtJNXJ2YAJnoAmvZdM2kQCoDMktlmIkEYHH0Z2k6zoVCoaFOJZh94eW3ZcUpkVq6ciNrXIje0jhQFLnblOCOItGtTzpgTTUWrK+mAGwi8t0DyjRiVtJmquZUyFhiCQH7TipE1Uf6pPpllqwHSm6JH6ers7SH4zdLEmR1cfdb2zQo3f2rTIWgLuFXRwJFUZecUbqmcXSDIba1b+smfewkB6RyxwqKVDmFaYcvs9z1ogcv7aINiSjtovssuuNs+zi1Pp3uxQpvyWCENGCgBCWkEcPeIJ7AhcEuJrMskKERxtyXWs9y5ad3WVxSSmdfHx0cfdPGE18IcMF7dYOOZwNP/dj7D8vcWqk8Yx4mR0c6trRtM+HY6O/dGQ1r9AzBhHxs6NzaxcvV+QUqzNV+kOVSCsma2A/uJ54Pha49436rzp3NfC70t9mcy8qMkLrQlaml8qP0evUXK89bbq6iu9r21cxSbpxaUWGcal5dNWNW2WsPUOtLa9RLLh1w22+5+t80w9JgwEA10KRyRFdSoGZkfPgQIHK8RycjI50hdtkbjHBrBXO2AFWnFcc5NdtTKZ0t2dUrSsHrgc/9D6f1mKG0EK38Xt9r4SRtZsSD2DnVbeGACRv/8e/WCloKAiIcK66zpWYVltNOchqD+djj+au67gkRewuAkKsmVRIqvs2z1O36L3vnb8eqinMth3KFsSsidwovPaVVf4IsulhThERNZ+hSD3fdloAGoppXV6Fj5V6NCgNsFPZmPUWWjArfcvW/A4bSYMBAecyPD1cqhK1Ur1ayLkszMppN2FuRfKGQrzeVVJmg7iZUTSJbbtsbjmd1i7sAi98Zvb5F6zF1EMGEScTWkk98ADIJJr8/RZKVFJl7xLkxk2Q9wfpMdFWxPQjYpVFPmyjLZOBWYYSEnQMOE2YbPT0lpJYCoHJLAKlbb+aumwUdAZ/JsOka7TEjZ7JxBtkNqsJ2aG3LM2ZWGYZZivL9UmHvW3UOCmxVbRTKQVe67xNJvJdolCCs79VV6FW9+tVYWV5OlSm3njoUpr2HoTQYMFA3FOOBqxPJF8rVum52usJ+76zIZoYtgMkx/9vdJqb83mAEbc6mBadn6HHA1ILTxlaCNVhboF1gdfXsWReHTchp0hnW10AUtIkP6+U8GIpiIRMPjwZX9N0JG7wbS5NaZ3qaO2ms2DBnE6lkAIvyg0CrKZ9gJUART6PJhCiQmh6dTqkNcsjbSkSdFK3EPfQSNYvzOQsgaHirwFrRGCOadpBbkXnli+XVxAcPjZJD1RzldT/vfD17ZiTYjYzTWhWgneuau/26jJgGAwayyBew7nbw2OdC/RPqz0U/2tt44guhEcbcX/kX56c/ZglemSkayXX2LFcm3mFBqyM44Db2gzzxhdG3/sPojELe4EBeUDMOBRMtLbSliGr5hixQSHTYLTnf/kE0bSadG7xZv16CbtFp00Md6btLpPJHdbGNR9JE45o8RmXA7M8CNImIWyTlgjBwAWQBWg5a1u4ltftIgxIi7xNaPIH/V9UAq4xpaLgrWnfkxm7V1fuwaxoth0attu53q+vOrzzI7n2PezbRHjyCYWkwYEDB5JgfJc4gcHY5xmKeajrwpA3eVIioAx4nrEj9fdA3MtS2HFU0hu7nPS6ypgfn6dSVG+u0HKLbAS5PIBQKhYZcMVqbia8w++N/nHnktOI5T8TyHAVymvhqVmM48fxJE2JzscYAzISjiYxGY6BshaVUiePfbOPQ6pKS3KFAFMisqAU58xuMMmBuYaDWvC5K8BaxmnGcciWbCs0J2jYWWnfziCgc4GDJ+LZTXTuYc02hBK0Be2NVbjgJwf7xnjT8cFyZluC+Rzbs/BfmPoZoGEqDAQM5qHEJXZ/9eij05Xz6ZdfghVD/bYWIumvwK6HhLBH1hwZcLs+ox4X0b8+fGXC5PM/1dQ2el6JRM2stRBaYieH4yGAX8+PDFyOwug427rweJnp9CRzd6gR2KzgcDjuYUEsh3HzlZoZMVhsQSxMLBdqOlVzUQLRCyYF/a517adZSeedAqQPDpPES5NulKShkJWASaTGTSLIwcYOMJnFSAAFrjsA3xWrFXbG5wr4n0GrehlZS3uBe0fgC6zfm91oY7isZrOA+ypWa3kNNToG6n6gavWEvQwp28gtzf4k7DaXBgAEFfSOBgasq1bQu/VMZImrHmQFX2D/8+t3cOKHPH8f25KjHRbeDk48zOea9OjDqcSH5nm9fR6GLdEmnYlEc3QcwsZUANmPxVnuGnGsiJSO0qZ8z2XqbgLUVAZBZyVJD6m4bmbwpQjjgzF9PySBn8mSwMoKjV/lJ6HwMVA4oAA6WjTKQSWeIZ2hThskHPohKPcxEUZK8AAiiiYMlI+lRO+VnWU3V7uiQrZwqKTBaPYQXGlZa/T5ij0P/dk/cVhM/0djQxQYqf3uvt+2B3mPQSBswkEXX4IXQIMDkmPul8TMXisijyjFYdw1eCA1O/rnbH8HrDuLyBM4XNugbCfUxP36V6/5XVrMX16/dsD/eG1ucsiiKQmxFNCmVq/KQVzhwgMQGsgAyJkhDYhVRLGCDVvaGQybuKt1zsQwWrEkSyBtZe7um0wEH9xRCBzn52GPpqbdAXlCTDgQBUSbNPY2ugigUF6sQU6wAgkrQJMKWXJwMIVg4lLTE5WQGOZMmx/4gcBjuVNjVdhAXkeBG9qPpo23pf6otHUJ++fPf+G++ukvVsWtFlSQKdaOB7MKV++7kKZQuuqK33Pp3+71Vgwa+25qgS8XRQBiWBgMGgG15qbcjou77E4/L5QmEQoGBq96vzpYO/78HI6qNQTon4Tx5L0F8eooYSRl7CzjaTEqJKS1d9OFPP5JEBsSPnjDnXQCi8ndbqPLfzd3ORT8mSZQ95NzLU0AtXy7KUJRl0qiOhhwycqEYFxAfUlpnGxSJeYWEMMkqyZyykvN3yGQqV9mWsx0e1rMaCFUeeGrVGADhyf2iMSjYh46PWlHZ7LFLh+PKrA8VYiHvi0+npnmrfBDdzzuHoTQYMABoiabdbj++C8VU02UZrH86VkDl0DV4XlooLLc8OeZ2T/xOtwjHhiQ74cthom8oTSTf6GkLG2sCsRXFUC9nZb8sw53vvqPIxvQ/3VRUARFBzFbNrkzAbP2j0xY19dF87HFFAzDVsnsIGi1BANFS6AiRSd6m1YxFLPKPPHTCrjYg31uFxtSxWhV/dIZ3s6yXgpAfSK749GZrfTZUpQDoDyswczwweCAy/nOoRjEqfaI6ZKF2olI5fb9iBeqY9z6qkkbKpQEDO8TkmNtP3nUxOea+dDRwYZAsF7VKSv3c3LA3V0EL7L/VG49/IHDhzOuf8V5Zhs7nPZZXvhHd3iZf5JtQ4OrknQUSIHY4MpsxypSxKEChA6DIHdDb3zw1sVmhPSC6TgmRGxnF8mp9vy3xy3WA9tOHl6/fyffq/eLoW18ejRVaKXoHH5+6/CaFEASa1IIaeqTVAqK8F0zWjnMh1dhQB4307qHh1uY9SM+rmxm6jsHrjr6kjBay83VW+ZU1JK208pi1jlxpUkNpMGBgx8gzPIBLKVKhlqyISL7QiHM8qy84HMSyfgHn2bPmK1cigKPHFTvylGf24t9EdNInOxCXxIxWWppbOLjJvbTqiLAf40iCgxbTjWjx8dsmsCHXHBxgtZDQpHqWhhe0HmB1A3AcFmK1BibsFolD48bNB6/cL6WhVBjshoDfbaVhVzWG0vH3odKwk8VU2bhcryqb1QFDaTBgoFHQqg7loiaz1bACRy95vxVRLQJ2afTZ2dFvRbC1spANlbTiSEifC/X/vds/levccW7U8e3RN5robuex3z0VvnyjeHjNCd3ejvkuy02nnzx8/WfNrN/KRzuIbdjTrFZjkMgiGx2pgZUDaZIZMk2Ym5C3SG/lJfdBK/cSIOIws5IoVoZs0qcJf7fQjaOPg8dI3mITEB4S5dvFlgYT9gzxRuxiZkiDXLfS0ChLQDUG6gdRadhVS0atg5db2/0Kn6zPJqHFnikNRkyDAQMKJsfcOZT3apetRqE6KdQqVJ5Zf1Gw5KQ29EGpqa2I7G4HR34+enVg9Ak7C5tqlIO9jc7WGD/wu79z+mw7dmlUsmNuZvP27bsZSDITI3z5xoFmmrXxhO1Pe47h6qTdjNVCfJnlDKSu/zDG6i1SJg6ZVa7mJrGSxmD5nU8WJVtaLYhNNBVtRAlkFO4nUqniSMh7Ss5nhlhOY2jO00uv62kMuiEX925lu8u3dW6na9cYTM+fa9W/o4x0Lax/txLub+p8fbgvcQ/7JEOynIi9X7ECFea9j3wbujCUBgMG2F7kb4v5uVmk/qxtoWvwvBS5em0+N7jb7Q8T9g+/BswtMj2dlVq9vcsx+O3eyLdG32qhE8L+0etJ4ivcWsW+BdMXrywTD4/+KEVqk3j45ShgoVdy0dtr3mRTOXo72wA2Xvv+AfvcAsspEkmwtGJHrUjV3kqauymSgIn3P4KDkvoNJpx2LBaSP/lekfzeTJHOkCza05pJpMmA0Ey3o/CWQJujRAfYJAVC+ayJtGIkbi7boDEBDelXLq/qXFbNMGL/q3/WkGl2DbuaYlA9thVmlUMX65OC+02CNhZ1x2aWfoO632lDfnMMpcGAgSpEfiHj08RYiUWiyyaohSvHJpUBXQNn1MgG/2ynyxPwSRC5qJS1hFW6h0KhIdfUPwsWIq9PEWcmVlCrwvHUJzNyXrDHk83YaTdhlgGmwhGmpu6BqLSIrrRYIEH0jXiaxyUXDgskV4lnS2Qta+RkmulpYmJJYYo00TjJJCCsF24v6m5TJO+zLgezmblYoedCZiWW1u0kZzCZsZYvQC1vIpp0K1o16+REPPR0KVumPpoPVk7yVKEl0rovqLCz75/DaPXCrLFcSdVM+oCimqcr0sMqv15tgwZ+C4bSYMBANrWywLqQ539cVCwQvuPBl5QG4dmjgVAoFPLlqBuYHLsoewIhn4SiOnjHhdTVa/Mwf+1qxLXWcvwXXv9iD3acntGzdkB02C+63RcjpO4k6egGOx2YsWEXAJrB5nryEZGU6eQnXfT0YJXlkx91LqdJIZDqHg2FQgGpGTICTjvNVtaapCGJVqsocOutu8S2tKmOBztUVkYAVycOLEXH9iIfb9Et0YxI8Un/oUfUD8m4TmWsHLSdlFnSKRIlVTq1C8ikdY0KmzpplrdfKxby5aTJ5r3qDBW1l8am0aZ+3Y1+Jxn59/2AXtPse7PaBysrNYf7ztthBEIaMJCFJpCxeyj0fzwN8+N/6v1W1PVsIMcOOZ9NpEQb0qhJpQSwt9EpEt06e+HrT1370y9+l499zHzlX1t48ybJkuwIsY3MCoBJpOPZ0ZZvqQUwFQgtyGugTQ7QpAkcNHOvSFw32w+a4/fiAGaBlKx2sZuIKy3bOllZUOY9sKKbm2k5QLKG47aIxVTyXDvKZXAcFGLFFSX2ED2+bOWRokDIysRE9wvbxhvuMCBRN0Zv5wH/1ehAO5n0gcN+i80sB8PSYOA9gfnxYXcRKlL05OIZtYGNfSNKRIOL7rWLqtnB7HqiN+eqUJIoCfvd7uHPucd+Csx+1T9dcjSNrzCzRHJj4to8d9eWUyeeemrANT1dIFldnhdOWxBQNQYgnSF2uUBjEFE1hlZRI4K1wYYpsH2kW0uvtBm/F1epjVJyvks8jVmJFVhZANqsZEo1BhOdFsCS6+d0OE8e2s6onyG5CUUUTyXliGvB/dQYgMU5vXiWbWmMde/uQwd8TespZ8bYyTG9voPytsuu9W3vt2/nPsZm1vQqDKXBwHsGUjaQsfZgRm1qQ9eZAddMzB75hVKwIcKnQoqdQVN3KnB2ZYFFpX30+honeujpKRk1sfCtV6NvrxMPe73BiKOQpjASfPl6EhmEw735cEUFAr2+gNSWNVBcAAAgAElEQVScN9Gv6hnWVXfD+o9nMqoi0e1wPWYBkya8oO2DKguzTEqjsqwkEExgP6UpZQVpFpLARiIbJhCNRd+4W9aoLwAiHUpQRabYsrBPzkV1wNaQUYrEbdG+XG6b1rbcOalwuUF2KC/LBesVLb6mWSo31rXxaLvUGu5QR/udP0tNoaNVLqy+9VR+ddt8F4Z7wsB7AVqvgc6VnONBScHXEioo6Olh+ojPh1+53tPD7HT2IG7vIL4k+QJHL3mvtzA9DTg7zdGFUje+xiYvWJCTOg0cR7izQAraz/7Rge988x0Z0xHsGywrkz3yuPmdNwsGNiH9z4G1l7xTFaIGqoSIuEVxRal9iDKujV1ghWoOhfr+0P2DQrXIGQp9Rf2odU/85Cf6PA1VmtkroByDU+Xxte3LRUfWNHvlKXKNdyjJalpqBW6rysvYVqztxL1SX+MqB2m4C2Yn36Du7IalwcB7E/PXrkZcR5Vz9OSYf9ajhC7iHx6fp28k4HEplgn1w5e/HPIt+nOaxJvTvcNZVwWbS0DY7716vHd6QRkxupCCY0MeFw4rllxFJfVPzHGyFTlJj2+0W0Q8AJzoyDaILagxg8tXvvm2jAypRVVjEARazXmN4bBJNfjPftX/8xSA0DXaXVzoAbp/93DRFT1/giBApkBj0Nk5LKfKRhGCfokokbLls7Ow1loHQsnO079cN3TNB5vuYo2hPHYvh60m6J7gd8/G3vAAz5qwf3wHBnIwlAYD7xkoGY8qvFcHstGLkxNh6bzysa9fk0tZjCO4PIFQyCchPSNNTWTdFdJZSdEvPvLGFJ2fCYVCodDoSRPQMXgh8NQjdNmQfAGPC7uACC6P154AmPaPzmd45Ldcju6bxbkCJp2fZJnVW/mLdxQfQ5rIQnRNaTA/OlPiBWDmB3eKrmjFYMfTJ03q4DkIH/D1KDJYfPysi24HHYgCINxwNAF0eD6tJ2P1SkRlKLWnFCFRVWWqYjRaGldDPqkg+uflomEqpDOU2uR3lQCxQoNytuW613Pfw/VLUfnpqlmqLodErS6V0lXtXMvZ+5yOWmc0lAYD7xlkYxp8ErjyhSjn52bzbZxHXRVGiAS9CgvT5TCz2Wi4xf8adh11wvVfLeP4oOr/sAqqBQIgSdjvDUaIy2QgEhydUqMHHG0A2E590l5EPJB2DA11AAKunrYSaSxiBruIDOZtD/KVsfTaG9nRrVnOB/kX/mkQbSKZN69EmImxodggNollQGQzqNI1ih1OvUGzy/xAQLIjAiIntCEdQkWDRSF0WtbSWdCfayd7t+NL+vzfysBlfPm6LatJka+8ZVfIHai8tirv7pxLsSEddygdle61Kjfa9rrxB7rLq8kbVa5j5UU2VksrmlfX/aGdcdt5DaXBwHsOfSMBD0FvNrKx6+jx/L3oXES3j4JcKKVPUhQIbzDC7BHFZHH6Dz7M2mXFjvG1BQFiF5UGkRXA5Qm88KiAtT801E1WaMViEJ2KRK58L56mCWe3DYudDjOm1ti3/27JDDKR6ZVTPUoYpIkO5UOGFMQzIJDSPcgf7HUpXggBep4u5kQWrNiLewhmElu0tKg/ujwBb9sWzbS309PDerctv5dkssEcAg99/Cs+KT9IMcXj4peurbElQIabb3YI5IV1MeF0eehsUdv6ozUtZf1yXDvZcFdq4wLdCXS1hDrYeOqT/bt3kC2lFapvrm177ZkJpNxTVLDrVPhy9xgV5q3DsmIoDQbeg+gafNHjCvtVtaGvP0fCNDkRVokaS1HUzBNQYxqe6Ffb931KiiuXQ//GnIQen9LADnbpxcGuhx42Y2q+vi7jtMuI2KBXcnW0q+OL/b7Hsn9v6VWO2HKESDemE5jMkGYpAdB6DJfnBWeZwD8RuDcVUbwQMky/tgoIIo5zId8JABJFyZQWqyCnIMOa4ufgSEvw3195W+WbXF+k97HmJhC7TigTiIpglkkwPzeLAJie97iKeacPvG+gtYWWJsRmgeSSTHbBcnG4ZQlhdR69546XPOFDWCrZCrbbdM1Op2MHpoat+rtWwK5K6H2FhqynSuF6f4Me3kshF9U/i6E0GHhPQlUblKTLvhHfcYXf0Y9PCXRQKCDdY5O5D/Pjl8JZGki/8qmY6aFvJDBw1et2u93/MXuU7xp80XME4mGv2/21ZQvxxdTR4zz6hLP9ED2SayockV1O58mDQOKNa+9/ghYz6RTdQ6FnjuDU2APSmuSI1VtEgi9HtXum1Z6TohlL7yfaNbcsOJpBzhC77PbfhAKhKrYBJBMygvaEvjjNQhRYYz3FY3/Y+y/h5Qxk5m+KSrykMobA6mX/9DoykH4lGGFDGVkEzAKsTX03FieeAbPJcrpHP97RhOQLvL8818Py388WX8rcJrkTW0EqGo1t3wqAXt/z1bJQK9A1MlcZI9kQI3wpKudQ1DRUQ7rvhiitJoWksePv5BU1UI3bYXJsKWoN+CiFkXJpwACgk7Q5Pz6siaas3Hh+fNgbPCK5pjgfGmHMfan1LFdWzw+95b844/SMfvz7o38TzVVqcPRLsXeOBs7Peb/yI5bjriHfkYsvTZGy9geee8f7+jvEEgAWTEnLqdHx/v/rmZfflqHXF/qSeXRw9K3H2pduLoPTZY1GNun9YuhPfn7+hSvxDIDTaYlGk2CGwx32O0tx0ydGz/1y9FKUNNjoPsidBVaUv/mHm4V3N1vOBoZXvV+7pk7aDHQ7Hz3lfOfKPyXYDgc72k1Ly8uFF+30PtE2FV7JpUg283CH+O7blbMU6sqnFNraxZXl6uIs26rnh66zNHaVqEPGVxikmqEaMmPlBZSijrmqX6fu49f9mDvvWF/3Wsdv+ET1rd9QGgz8GkBlctQiLxg0bapmepgfH/Zeb+HIac9sMBgBl2d04OpoEI+iYxTyQLgkifBbd5lZwt4Oy6XUzfZuR3wmBrg6OfKbrqlwRKGFuLbOzfJnZivZYlRYzGIytW3uoBmHqCoH5lxRR4ujW47NaEkg7B2uI6ZIZJvDuq6Uz110dHYuLyzk4jHMZtKphidEgNiowpcF2F2loSaUk2cVtvvdUBFKsW32f2NnL3qoBs6+9x2rR/U0UDufovoBDfeEgV8T1MwXqcv0EPDM+scm6Rq8EHrmCOHg1YGAUstqdO58KJS1SvSNhEI+CTUA4jzhCI8NhUKhwBMpkLLBEsqKAh4X8cek0xaAyAJT4QgQ/vpXvx9xnfF6nJgP2KDTEwgFPE7M4PCMKvwQredGT1uwH8TOB46KOPp7sdDrC3g6S57F+ZR0wkaKWEaNtcwpCSKFGgMQX4q8U1JOSgMBHJ0cFsFka22nXROwIFtoN2FvdT31+8dVjcEMkCrRGIQyn5U15dH+9OEKZ9oijWGbTJPSCPlzoZBUcvXgSIXsib1EBSaGCpmf5bo0FnuZGVj6UHufl1iEPbAx7E3ARB2xmYalwcCvASpbEXTsEAWQfD78l44GXuSllxhoCQanAZdn6HjwomJOcHlGB65+nRcvDEZzJoaeHo48o8742v/ovjgDuHockekWpRQW3/i8+3IMoNvBY58bXf2L0euFiRLtZ59vu/JK0bo6HmPzLUC1VrSbSbVwxOIU4tGlh+jY5KGTzl/8U/RuAppUmSq22jOrcVv3w+sz7xY9mSAibpHWDbcUyDTx8AHbu2vrWbalU2ccNya0BgiTyt0gCjpEkxqqAnOLmFrTVuFqPA1DI7FfLA11SKY9OP7u/ezVDLttmyptNuXGr2NJdaMadeE+ym3D0mDg1xEaK8LkmPfqQEA58bs6leO/rwcsp0dzRonvLBIJer3BSCQYnAaXZ9RD8GIYhycQCgU8BP9x43hkLjo55kexZwTOzk9na1NMjl2MK2aF88Rg5qKStXk5htMTCIWGiDG7BGBvA5daX9uGefnqKxE7TjtmzVF+6S2E5rx/YzlFfIU5IbXcxOocMzGmwtFNpUJlxvY+m4hAZjUOlGoMgJzBJAIIRYGKgijIiBneja/nxLtMgcZgAmRbazuO5mKNobmVU06TZlsr0Bg4NPRMO9ui2nNWaTsBu1RMklkOvUNnS+I3DzTA0lDE7b9nB0fdZZQu6b2HyraHmgww++0VlRoD7u9J31AaDPyaoBxfZN9IzqtAi2rY7/tywNN1fXRskhzNg+QLBTwuxeFwYfD0mQFXp0uhJu46epybv7qp6CAqJVTXZ/9U4ggAkxPh3t9Uh33SpSRqhnwSki/0lcEu+NBjdhZ/eVtpIZ0f7KJvJPBJmRRLpyRliDS0egIKDeUx3+8rjFEmen0vPCogCJhiiUQif3jfBGRo/o3N9Rx/pFgufSGRRhSQi+z8YkYQtokWSAOCpQnWSpgkNlf5eTQbnihgs4BV0U0AuHvxSpzm7F17Gf2g2n2xtJ1MPFxMkqkP4cDUxSslcZQbn/9GlXOXG7aMiKpVGtUhJyrk4te9jPuOUnqiCtQIe0bboPu5HBqisd0vmoci1MoKb8DAA4qszXlyzO2fzfNFAgWBi9IzTI65J456UhD2u5Wr0rne8F98Zsm5Fpnxfj7oeipwwfbDyEIEIt4vHg09AyvX048vvDT38d5I0OsOArik3sil8ef6zkz8iKl4BPxj/aGRjzgu/n9//1fD/hvrrIWHxwMv8pL3JsDyuyniK/kZAbACd+KkgNWg/7VRi4xw6y9/QschYelumin/y+12pXb2gQPyaqqZdBLb7z1vCb+yAmzeiOaHylTQADIyglioN6RJsY0XQYRMeqU4dSI3ZvaDILOezF5QRhMQUtlUEpmUWKKyHPidwxs/qU7s1wk1gFLe0L178vHdnLsmVJ9HUJT/uXPNoL5Mzl0Kw8yNVqT97HyW0iDKWpe0LWpd8+69xlIYgZAGDGyHIr5IJsfc7on+nHtCQTgYjPYOeVwATgc8/ieSZfnGTBKIEQl63RdnwIJT6pn1u/1hkiePR6KRYHAKoOecRCfhxbMf+r7X7Q3H6ehxIZ3DP/xXKRstUwpzc0z1eLCUO6kLdJwLhUK+XjWibyocyZdMiF4cncnQxMoMS3fB/hDAchxk6P/3v2EivQky6+FXVpSzu1UpH9HRIeaGz7shBM0/pmJLg6XT80IH2xz2K2ghAiCou5HOGIUcjpulAzVCY6gsB3Jztp0LnChuevBoJe7s/YTdsx/UPfIen4arPMFXNgzsK+N/bg178Brr+5YNpcHAexTz48N5b0QwQvirSrbE/PhLwSNSji9yfm4Wqb8PugYvBAZasv4FO9Knnh68EAp4nGtJZudiR4/gtGOXAopzoVdytVlYWB0cluh0whsRlxOHEpJw5HLYsnAXDj3l67XAQz1LawOBkT/ulyLfuxLh5JBkh2M9uNSEiHTScuiNb74tg8zSZbfb7Z8iabNCz5Bkx9KsTOrr7bY1m/gdn0/i4UdNtEghheC59TBnJFpEhXKpvbcXyRf6990iCZvDBI88spEx2510OzgkIEMz2NsVx4qs/KMEQgoidoXEmuTCf76mfwZXIGQ1AsTOjrx4zn2QASXCWtCtuLkXBnKd7dZ2oHTae7/4v/+1uGm/HjNHTXOXkUDVmA0aFXZQNO+2Lv/GhjvsffxENXpDZTG8T4z/+x+G0mDgPYxsmmXA46KnM/hSNsmypT/HF9k1+KJnVg13eGnuuCsyFwVcnzyrXPQGV5YtjsicNopQrYAlmkmuvOs86kquYJG8PWZOnjse9Lr9YUiy1n9hsCsW3wLbMwqPpD9sweWEldUkjr4v+44HvW5vMIrtWHIjyWEzzSKOc6FQKHC2lfUEric/dPQI9i2FbtL/L9YPmdO88eO+EV/n2ylil93+MK5ODpsAjh4kI8Dhpz6wyBv+4X859YhVvB1LwzvTScdh8xqA4iXYhPgya0BbwbuSt4gnsCj8iAs/2RDKnNYFkHM5V5mFpbx41jUqZFTDQ7ZvSUvBpDfPtuKmyjpe2nHWN0pXmPnZj4svVpi6giwsuqWVQFVKo4abDYrmLbeM+uat/CrqGLBKPIhCfb+ZMXYIQ2kw8GuArsELoS+cVhWC3JXQhTPXhhUzhKJfXBjpP67eP/ThAZdiCmhrT8YIXwxGiKaVOhRdR4/zVjiytAwzF//yeiQddz77nAPg8RFFR+ltJjHhdru/tiTkJgudcyTjkSjhVxYsxC4Pzz2najOn/zjw6TYAGWKX3W6398oqQORbr9+FD/33HgcAyTeuWR4hPj9J358PdavLjCwQWeBHL3vnPhfoMUHslWCEGJEr124nDn30fSJAOhZbXmYmxoYMFppz2/gGvT7fsdxLksGCsGEWAMcBZFW2m3p+QwAEGyI2sVDgH3qoGpkgA7JazEJzKfsxrUfhUO/WWmzYqGMceV6fv6OCLKw1OH+XzAl1oBrOwUbpGY3Cg2gSqHvNu2qzqe/3x1AaDPx6QEvVpCKbbJnnbKKvX5qdW4JIcPw6A2e6Jv8yGOVjakJme1pNwfCHiaEkVEiz09YWThwtCKv866ktJUvi37SkYGFiHpj84uVY+9lAKBTwJOM4PE/NeVW/yY8mYkDbYVoE7A6XMoYVZ3f8ypUI4YvBbJ6j3fZOp2t6YhI6HsrWvnCeboeWZiC2voXJhnROwnX21EpCbE9HS8IFzIcwyyZBwNZGj4Mpv/8WCAJmEEFIskmKRz8hxWxysyqA09P/KgPyOhnWCwcU128DhVUtyqL6/bKqlrrFPyvzQ+pFfVv7XygppjX9pR1mT1RAkbjduTBouATVNZbsT1RguCp9tw9ixukeKGd1fMuG0mDgPQxNmqU3eNxXVEZCk2yZK5/d13/86i/XcPUsvhkJet3+NU/g659Ve1lMivsg5JPodEHY7/aGn/D9u5OFk05OTDnVhMIPnWqH5rDX7Xb7p+3Slz/bBV2Dn+9lTeGFcnk8Ekd+Pnp5jpkYy2niMZXMKdHxEVczHOvRDByPshAhPDEJLWRMlu5eF9HryxBbIOwfXcs4Tx2G2xC5cmOTzMyPs8UucHkCSgBE6jYnpd/rbCJ5j+lb6l1ZJiVgNtN0uM0KLvdga/uSKa0KYLNNR9oKAiJspVUrgmqTaN72C6kyW6sBG6RQ8D8AS5tOg8TEy0V8mMD79jJ7Qjd8/f6K6p1nBup+3g2UyjxdQftAZ5w2HDvUn4yUSwPvYZQUmCiBNtmyH8B5lJ9fRRDMzwdCqkahaaN4CvpGAnPD3iAAs3PO81J4YnJk5EIIUCIeLC1Iz/TB/CELHPGFvtE3Oeb2h725fMpOT+CSOvrg4OSX3P/hhuPcaNd3Ruef8Z287H+jm+TylYl72D88+Kw4/cNl+4z92dGPXx39/qIl2hyemOyHjXRyZioCtLej5D3GiMaiEFXnsPKQRbi9JnJApaR4ofMfXl7o8H1qze8vOI93nDx076215FoK7qxkwNYRfXtZyDkNUutYBdIyVmg63rIxuyaDLOuc6dOKklIpUbO62lLViBqrjcR6wZXCeeWC/wEkb2c/iZAlsGg990drl79Z1bIqJMI1Kkdu73WFuhMOtx32PYM9y36sFdovbvfyP0thWBoM/Noim2ypxhYo6DozwDpryx8501XUZvR0PvhOiVJQqk68NPcxz6w/V0e7a2OdGTzP9QHc1VjRc8UvfBIL+egKJidu5ML6Y5f9YYjNsLzMRprO9zvmYsysxIkGR78V6TTH27uXWsN+//XOnPN+eZnDHUjnpNypWuSU1EnCfHtDZktW4ijd7rEZE5jSY/6pQ4VBhPGEObmWyW8l037/VFbYSkNnrZCQARKwPrsmq2d0S1FmhEA2OLHCLiQ6GnZKKdIYKs9bBI2+s6qnMbxdrvKG7rlW+W//G/PL4QFddvXYiVZ036M3dD9TMapmt2EoDQZ+XZFLtlSRJXM88/s2jnz6qS69Nmu352FyzF1Y7MoxeCHkI+sKuXykR8lohJ/eWIZpvxIGEfa73e6xSSYnwi5pLe84uXT0hQ9m/w7V7Il24FRvOzMXveGccIyzECE2Q9oEq4stDyH5fD0gwp1VopNhmsBKb297hhtvCNgFUmgIGMKvvQ3plXWtJiMAwubcKhZBzv6M+dipnP4RvnglkeVdwIyjGRTp3HzubIsqfG1tonqxMM5AUSlErc8iE6vS1KCP0uROXexYMXm0o7p2tUoU3TZ1JEDuhs9+/zhHdohtH+TBerrdVkbr+IUx3BMGfl3RNfiiZ9jrdgO4JEnJreiCrjMDrjk1srG4TZwo9I34Jtwq8SOST/Fi9I2EQiPZoefHh73uoNJlhvN5Kspw2O8OuzyBC2euzU6pRbIic7fbIXZ5NAZcdrsvgzTkmb0YnKfXF/jAJe8/3OHWKoDjXGj0wLD3mxGQ794m7PcDgoV2mbdvQwYyTE0tA0srQjpZvNGIMuIBkhoGBiUiIZWT5C1Pe4689jeRG0UdmwQyskyKfOmJzf/0g7TKI7m+oh99qFzNbOrerAuVkjs12JFiAnR8aW+qXOpKgm2tx7oNGsKTuB+YH6tE5dkbsqT3auhDqUOq9Leo8gs0qlwaMFCI+fHhV49eqLloUS7yQfKFRvrmx4df4sULg10wP/6n3qDqjXCgCF47rmcDFwa75seHX7q+HpmO0XEu8N/+2Ht1jcgCANLQ2X+5eKVryLNwMbigTiH5hm76L0a1sx47wZ2b+QJWWbS3O1zNsX9+F1mg94tDb/v/z/ncTiCAjc4MKxksEE9BmTKVWuyUsVcNODCZSO9UppcbeztYrCQT1Yx3OBT6f9SPlatcFm2+NTExlGu/bRvdBrWu5L6gUXpGuYetMH6tU1dpE7pfKF1eTWvT7V79r5DhnjBgII/58WG39+rAczUfNSfH/LOeQCgUCvnwD4/Pd50Z4Oq1eebHh73f73S6PIFQyCexBi7PUC9wPJulGZk++fxpCyaYW2R+XQl9CHhmL4bvwszF4DqSL3SuAyCc0xhM7/uki54TcOsmcTqO2QCEVruFXt85B6aE5czhTdWQMOW/GEWWwXrC4eAgMqyxkCCZUjUG0NEYWg7mP+tqDIqRUihJWNSH2j1duQhWfahyv0xWq63cndy+jTJxjSb994wLoA7sdnBA5fF3aOTfV2Efu/32toWhNBgwkIfC+VQ7jfDkRFg6r1bN7JciV6+pWsNrrwaPD3182TxwpovJibDr2VEPwW9PuZ7oDV/6nBo7KfV/ILFFbDI6cinw/BEWv6PyNyS2II3LBj8evrx06KQTl+d5NV4z/avvRZi+iQVMVjZuKTJ8NZ5kyn/5Numtxz78aFtbMWti+2NrpbZ77WZ7oEvMXlm7l7+uu2EqA8mlCYsVsVtbbzWbXrVKg3UblVEbPVCrNKqGzFj3c+UG+1wd2W+m/geRs6ECav3Gd/gbYsQ0GDBQDQq9D+qViaOe2WAwAu3tfLhfbbn0FpGw1x2k2+H4u7DrVOv3V1Z4dexqOAyMRgAisSmA9Tc5CnB7JQO3v+p2L3S3MLOmjrMpQ0fvyjtT4dcjwBtRCL4CBeb4JJAgzTrpXOlGtmBralQJuRCs/Naj9l9E4kmZ1NSUap63HCSZVQpyG4hA0yPtTTPzpVYHm8jGVlVbTW5pVboMGgYZMNlt6XhJUkXtiI9NUtY71ZDogcrYdsz3QHxfNdilBNcK36B22H2baVmEHcav1OGIMSwNBgxsj2Lvg3o5HLw6EAiFAtLGcpZI6otfvLiA0xMIBTzJ2OrbtKz+FFdLNMx5n4TkC4XOtYFjKBTwOFm7/NdzgHIGFj7mk5hJ4uiXcJ0968Jpg3eWP+xEhANtKAWuJGt5aZxRPQoybLVZ7NKopwM5wXy8pa0JYOvhF35LOSUkNWYEARGEtoMmGWFmWqmI3fHbTu3dZAZZRrSWOWMIkM2VyC1Nt05VA1Bp3JzGINr0dsL27grL18JZczzLbqDcafg9cEquSv2s0d2wc1tLqTJRq1dib76aXbUqVfPIhtJgwMC2KPU+qDek84OMD3tnf7/XTvvZQCh0bnGaeOtHznTRNfiZ3kSG6TAfOWZG6u/r65fCl8Yv/WQFe+zbbv80HO+ZemsdWE7IiEc/PjdLu0wiGnYNfPgQrDVB2803FsjAxgpE5qLQqhSUspQkBQpAXhXIrCTj4dHvm3A0s7QQW8gANL/78s9K/uBtrWyBvHIvnbPfyyz9czbaUiaTvZ5JKMoNgGjVDCEDZMjX3aamDAaTTu2psvtuUUyEfrvMut6+tzyjv6iStlWGaewmyonMB5TZcG+8J/eXKmOXvhpdReT+PqmhNBgwsB3UspYKnEdduc+uo87JV4PHfRc++6knsFzxut2XY8DqFa9bKXFtEiFy5UqEN96EvhHf8eArMxAnRvR6lOlplpP86i+/eRcyv/DO9Z2+m2b1FpHgaDDCchxWWEliwYQJhbb6yjKYoamEfkiGg0OhUOicI38tGmMJ7NLTrSCeGH2uU0Ol0GEFzGbWV7IOBQtmxKLdTig9nCsqgl4aglxflGM6uybNXNVuh/rtBNvZTxdLfgGHblsd3K044/6OHtg/KJJ2FXix9hi78Q2WMwg1cNjdqz2h+7kCDKXBgIHtkKtMARCdi2hu/XQi7DrqZH5u1vVUQGF77JVcKvvjkGsjK0djlz+vOjXMdJwLhUI+parE8jK3Hv/QAbD+tmf2leU/etJWNPcmzsfsOU7nDnv2upYC0gaY4F7wr14bu7yGDadn1OPEZm9H3MTsOHYQhNnRoNAJWd7GpQTQZKUtax5oEbDYMgACWLCaEEBueH5kBTRsLnn9yneLAzRlFkpdG0ILrjMletF2rpUHl/xxz7CttNu2wR4Y4UtDJRo+Y02S/j5qUTX9PhtKgwED20LxLMwDTE6EXQNnsumSwR8ucvxo1+SrQZSLff3S1BsCwPzYcxcj7c+HQqEhF0As6HX78fnez9L3/mqevi8HJFUDWDzSLrI1Fzzuu3DUvH5IpVB0OsxAm4PoWxuYVOVjKS5CClA1CJwAACAASURBVBKEL4ezmoSrp9tEGli/cjFMnHWiwdFglPW1ZTZpfeSHr96BdArr+77mkwp4GzfvsZJBAB76bTZZW88WoErSKu4oklHn6FWmZVvVFoAdQhbaS9fgGrCUPKaOu2SvUY23/gHSWh4I2cl9VQerMSrsE4eUoTQYMLA9+kZ8x4OKzwFNsUyXxCxhv9v/IyLBVyeVlkO2GaUApmznw+8DPjTgoldytrcj9ff1fXm0++4Vr9vt9objgOQLXXjyYZGm22rh7rsqhWI0loLDv/+HLpJbpM2oUQyK9nBsyOPIHYkjU4q33loc6CCDeGLkI+Y764BAZkJJAHFJkhMrCGBGDU5IvVtEEBXTMjmKxadvdfc6oBJLF8KEnkQrtxOvLPBIK5Y92A/Ty8VrkNciwX8srR+++0upAuUEWEMEW62Cec8E+bZmif2JXdIztqX2ui8wlAYDBqpB34habqogtP7oF4YlpHOeZwOhwNG5SYD5dZB8PglXv1ozu2vwQuhTRFtczvDEJDiePKakQvgkHN3hS+Pzjp4uWrN0T67ObGkrn8SH2tZTCDLWJ0JDEi7PC90itJ09K178hwSOU86CJT5+qhVEHj59GByeJ+0AmZtfvhQBEGTS6roG3k8UE1YZrP1nOCgDGx299DwEgLO3E9oOi4gCtCACGVVXMTkOAaacEWKjULdQoedmMOkmNAAg885qUdmK+4vIWLXsTsADmM6wk5IZVT5mOSGae1e7ajK5L99ITplr7KPtQ3uSQSNtQA/z48Pe4HFtYen58WHv3PnQc3OFNybH3H58oZE+pYvi7terSD055r50NFAHb1LRqubOb1ftulHdCgcofTT1ic5cG/YG1yEm+UIjfZNjbv+sJ3Dh6KvuSz/PUkK7POcIXs6HQpjpfTH0pb7JMffXV53R1s8Ejl7yvh6RPhca6Zv8c/dfTBWIUHvro8nV28nsNRFrhgRghlSr07YaVfIMBWjmRCc3bxWuXYQtbFbWNzDpynMHzTG2LRBhFUlUjHOsiZihmsb1MD2IJekV9cAVCl1QP1amkeYB4W8uwg7Zryt3rEB1UAeLdvWLLDfCA/GNKCjHDLHfnsiwNBgoi7C/9MjVNfiix5X17zM/fiks+Ub6YHLMGzzuU8tMz/oLq0ACkxNIx/Opig8Syj1a30jowmAXXYMXQr6ToBA1+PGFLgx2zU/8iMXfDIR8EkAkeDmicC9IvpCvlxRTaltfjxmlRFaM8Ne+AX1/ImVd6u3tSL5Q6NLIw9B+TrE9nELRGMCWwnbiYHQdcPW6LAAJbt6i7Vjh6jMgs75Bs4mOc6FzrYV3jz1/+o6uxuDsEFEM9W12RKFQYxAKAhSKGBowb2/er2bby5FEVYbQovlhO42hOvaIpZoMDY3Hg2K6KK17tHOqg18T3ipdPCgBtobSYKAcJJ8PXbXhvKQ68CdfDeJ5rg8lPNCjVmzoGjwvReYKyirNj1+aPfpcv0ZrmB8fHh4fH1OKQw+PzzOp+awge8Wt0i1PjnmDEcL+3I8Fd5Uhx8aGNe3VEV+d0yxk2K3TLbeSQbd77BualcyPD7u/VvHRYH58WOGKhB5faMQ8Ouh2/1nY5ooTVZwaAY8TO6lbP45I/c7xS1O5nmG/N7iojHshFPDYLruHP+d9qwXJF5DsLFs8z/XNjw+PXk8Su+x2/7Hb/bVHzrZDz5DHRQrWbyoriUxFkkAbdnqOrdyy2YCOXid0P3UCQMAMZFj92djlDK0imLuddGCCW69c1+eBji5lEA4eAVbiZGTERzrzN+UCqV8sqSsyS28jCktZfMpczzdYK3urFLpKRcnYh+4nt9P+zLLbFnUstZxipLXzPxBCtCF4UDRFDKXBQCX0jeiqDX0jPil8aXxy/NKs50WV8mhEU7Fhfm5WDerLXrh2lYEzXX39x9VoQQAiwbl+5fhO0OueyH1+VRX5fvLH+7FJ6BsJeFxZ/0COojF7VxkyzHk17CDP4XiesOoZmBzzXh0IFAxasJLR00D48qKvYCUnfZUebey8NygMhUIBj4vuRf/Y5O13U7CKvc8zm9VvMNNijV5XFSy6HdmohdDo6SQ/mpgHRXEYYoGZGGG/NwzS5wZ/OuwNCs97XEi+0dPr0Ow61IZj+mIwQgpOSJrIR5kV4iz2nXOsrwNLU/faeSgZU24JApsyW7fD7WYSGTjwbz8jLTmzf/wWAdAJaJTvmXtsiCJmkeYPfEDbQtDtoCCtEcO1VuPTcFqbdK83GhZ76bVa4ise0HSG6uVxA8VYTf6IhsyyZ+GiOxT5DxZnl6E0GKiEvhEfl4pdDdD3nIegP3j8fGmIwuSY23t14EXtDVVnUDIXJ3JaQ/b83nVmwJX7fPQ4s3PqfOqHrsELJfEI+cTHrsHzmkGl/r5cgxyH43MeV/ZpcqpNAfVCdnbHsRbs0tAR/9ikupKYdtJvfN7tDUYiQa/GSOGfBWYuKiWmZmK88SaA2WKdeSUYUV0W3mCEGBANerPNwn63+4uTyozJH/nHxj7vdrvd7ouKduM4N3o6SdjvvhiBmVeCx30jfaf/4DjcuqgO5TgINPf/dcCj0WBE2LybSppNAsDqMlNhlacoKQMkb7O8zCZw1+8PE02C5WB7O7YmsG0eyEnp9hNYFXEfnV4nkyGVYT38A60XQ4Z0hdN/6adad0K5atYGU5d1+0ZlkYyXKiRybU60B/pAXKu0q14kl3M0bBsjuXM0KsFE9/NOWtaHXVJM637hhtJgoDL6nhu4+lKJ2tB1ZsCVl9A5zI8Pq0597cVrVyMRNV8xjEZrqDzvSGDgqrfYZaGBOqTb7Q/n9YzcnFoOxwIFIeu0yHoUSvEhXfvK/Nj5y2tSSfmJaASkcz25ZicfX47LpFKms0NSwY3NNk8gFPL1YuGEcnXa73Z7ryyTjEfD4QLtJHZ59HoSaO9VciOVaImb2ds9Qx7bvU4ri2/y/ddVX4m5xdEJrLx+bSOVLrexCJzQRDyIQPLe8jIrGYQNNnJSeuUmiRw3tO4woFdNW4veJwujJ3ZPpKbn9dgpd4LFv76PQQ17abooknalUqSy+6DoYlGDnazEQBEarpju5IUbSoOBbdA1+OLA1ZfGo9u3ZP7a1UiJJjF/7SqKn0BJI6xWa1DKVIdCoZDveLBUbclZ+EMhnWLWBXpCToOYHHOrbpBQIGt90EPfiI9LEwXO8vlXfxR3/MFIafmJzjTh//pMyNeLne4cT5Ec/94vn/Mt/qdpC72+PzoEyCt/800mJ6a6bdyc1o7sPO3E1QkijnMBjwvpnIRqM19+MxxFScI815s/rE9/m6d6F5qIXVZYqwFSa7GFLYCYRoSKYALBqjIPiAdtN/PhHdnxmmktEk8yrabsXQErB4tejyxo+xcie3Hqh6v5i/ani3kuBavpvSca6ju6lfaqT0I08KReZQrDtkTRda9wH+oNjaW10DbYJQrq3YOhNBjYFl2DLw5c9QcjVTQsdSTMX7uq9WIUeijKY3JM376gGSZL0ajbUtvgVXXp83OzBdaREvuEpvtzAz9/A21E39EjnHxc/awtP5HK4PhgH31/IsFymtm5u4Dokgj/T6Gm9WYZuKs4ydM/9IRmuZMlURKPuBwOOBy/Pk9kATLELnt/uM7iZBjbJzoEgBUgwcodwt+e0hzWY68Ep9goWbQM4FA9C5hbONZJE8gJ9UEy1s/aNVu6alnYRBHv2m1qNcdbLZPQ1MHSTlRgPBAKbxXtePG/KyF1TpQ1h5SHThSFYKrZ8bE9DnyqjkjI+o5uVfaqRgjVMftOUNOM9elS9xdVRmCU5kNu+2aq/9L3JwylwUAV6Bp8sdLJPIv58eFi+T1/7erxAtuDRppXQp6CUUPC2HVmwKVmT2goGnPRmEUDZN0bl5BcuaeYVQpYu1+aO+4qyYMoeOJnj7Aw/W7uwtwib2WrRGnLT7QdZO2yyu8YX2uPzN0FaB65NNT2sxtsppjyfzcFgokMKz+LsJIGHj8ssLUYSXQ4HZuryCCAgIjpnRizMaxL/2VJs2Utp7PRhdltJLUJMgdNhQmWJiRf4KlH1BDC1BqRBZJoSmIuvFxE+6hFUS69bnZiBYKm4h8LmqYrJVRUDb3U0HR9jo8O/SrZCjaqtYVVg50bAHZPIdjWmNEQX0k1KsX+xIMbqrItdvLNGuROBgxkUcQCpXI65fI1suxUuU/z48Peq2sc/3xopG9+fNj7i+OuxaMfXAq+tvaB0Ldf4hufd38vTZtIZAHxEMfOBh55xTuRomPwhebxl2MyB5zI77KWwSKSTGNqRs7QBHKaNDT3h/6XxB+OXs8AognSZHA97+GVb0WIg4jDzJpIPFEto5FgyhegcjhYjxEXaGrmg51M30IQs2UqBZpbIK4K6Rx9n0kgZx4wQ5EeUMDFVEjMJCgeDe0Vm1VeTxS0FWhvY325WDMQBJrk0uc7YGGjESSSR05aFt8oHkj0BL6tqqHbkjtpF6pFlXRGdfAd1d2mAurovhOmpiqxT0RT5SetbGnQ7VVf2Gm5Ve39WzIsDQYMlEGhfaVM+Ylc4zMDrqlwhP/8Wq6y8h//oXP5Dgef/agZtu66Bs7wG+0IMkuXX35bJgPxKGsZLJDK0GyiaVPEZk6lVcfB5oRb0RgA0UYGaG6JBSMo5oIMsQTxNR2NQQ05OPa8i4LsR1mTDBmLcQ9kyCSYvgVoClvLbMbzwju3KWkdCnmNQUAEwUyLUnNCAAGrCZPGlyCDUBQ2t57Q3lX+t1yiMSiz62lEGo1B2ImDYjHRXmpTaauHtrSao1uptKgsgarEfWEMLPe8ugGVpX3LldXQ/Vw6+F6i8qpK726rP1X5BVWjMZR+3oP3YygNBgxkURKT0TV4QVNsQlN+Yn582O0eiw5eCAyojITz165GJF/owv/6yQNgPgzA018ZOs6Ni/+UgqYPXBjs6jp6kt/qtpja1QHtUuBcJweOYoXNNA/bMpnNlEs6lRWD2ULYIu09jwNsTocB2pRa2HZoyUo8uw0EOPy0pzMbcnDrlTmRJhBa2kyowrnx8kQGATlFXAmSkKGJRJpMEg4eyrdS1ikgFNWQrGF/E7ptes1l3YeqruiU8NhMrFQpqdeEsUu5l/uWCqL0eXcorpTu5d5hqXRsCKoRtJW/2dK72/4alDbe+Te7Z3EthtJgwECdCPvHJkv1jP/unIszkvrT0xfUNA1Fjejrl27IA4+vAYi4nn2u649/17H6TuuGCRssrJvtm5b2j3klVeQtHekEEJqI/epoR24fOPb7DwuQed9HHawJKHTPR+y0NcGd14ILakyDyWIVBZotyJlMGoRCL77FTjMgY38q0JPVPFwdal/xgHr+NrVkIysF7YlcpV4SABvF9EhbPHYAWYZ7qs1FtNKsSGYZueUR7BzOyfSCrbIyAbU8s642r4INutogirOu0tHu1VawqgIaJeN3mwrifuklO3Rq7Fw07p6gLRcCmVNQHlx6D0NpMGCgPpSh2Y5Nz6sUT2Spr4HUHQD6npMW/4viRDf3v8hL7rHHv/pU8yop1iFB6khHcvrCeGuXWrE6soAo0pSCWDY0UoTNXy7LiPzqezEl0AFSRBZYySAcBEiDAOlkYjPNehIS9wA5XXCsT2YdEPHXvdPZs3ZkSfWMZDbU83d6jZjC2VDgJlCpl2TA9NhqkRtXJppSHvCFRwUEyCQ0foe1t0xx7qR0bQwpragXxaImQs4T0YCiVCpuXYk0cDQ9bCuPdy6wGyLy75cMe0AFpxbV8FtUk1VRH3FW9b6wRsFQGgwYqBe6NNuOnq5scqaG6/rYL9R2R4+oDBOv/pna4Qt/cbbdgrMTeiWWSLb/yz+u8EgHFhNOB60CGQF6fKHAWTuQgdg/L0OmiV5fwKMhkjaDfM/Zm+WKONFjE4tO7mpSg1kEHj/VDIKAue1MUREr7WZzaugTB8rdg+ZmuPuuObdxZe9mUthbSE28/LaMDB09TtWC0SnZs6meQvaAL5ZmsR20AplMkd9BNul7IvKwt1S8rQOTqSBnc3f22W3l8c4F9j45tupa3UtN9zsfXBf3JeKhGltF9Y9c/fdYzYvdpVdhKA0Gfj2hpIdOjrl3ZIvuG/Fx6a/+anh4PDa3mDcwKCjHdV2Mrg+7YC0DU+FFySPNdLWvEVki1URqlfUmmlvo4dI4Tz1hx96GA6cVzFYWv+r9oYkjWc1AEIHoVAyzgMnM4mIy8/+z9/bBbd3nvefn8AAgSEIiIUqEZYISwVhKorKOwzpkTZdJT9JGE08Ds1nK66tO4EnWiYXUswynN55OkZnLnQ06vW7vMtpxAybutg06Zb0mN6WR1r5qm6KNYrqkEzVWGcWRXIIWAdsgRZGUQBLEC8/+cQ7eXwi+SZSF72Ri4Iff2wGo3/P8npfvE0UAbSUiiArFkwxE4xpMl/XVArJMdPG1G5k+YhmQPq4EMr598a3k7f9Y+34hLZdSFImvU6lZUzsk8yNEgNByKkRxftKvagqhD34uUfcqGZWZZJbUHTFVKobbm6kwSUFIrbghsXQojY+rWE5lCtFYRs6m8rJx60XU73RsX+4WCXIsHhaQ93WRblnYgqPhtgSQ7ipujd5QVhrKuDvR2HP2bE9jR18OG1VJCE4G8L40DnQ81vXGG13P9LCUL34unev69bFAlvMiAVOtjmiM1lZqvG9iuXj0i90GkKGmltoK1kJMTvrcdrs3RGiRQ6cOhAWiCRG6npglkrCzR2WiUcJanQBiDSaRdYhHqRQQlft9jCATC4kTRsw1Ent/qDzN2htvJ4+dKxM30jMn43FiUdaSkjw5PC0LI/sMWxl0z6beKZmWSUSuBteUAdpfMyb7yKmZSytsraKk0hUy1dJnczijbt46FunbcjkuhFLkbt4N78hTlHjJ3rNhoenIu8nbtfMd/+sqKw13PZREgPGslsL37+SnxbttOHyL2Mr4wEivtbe3V2WOzH7g8YH0txkEk6lK2skaVX/+hLXniSf6L0Rg0mn90pNW+8zJrvPPjry3GCc4bLVa7edWlJG9L13EICneCbPRyMc6G8cH1DrXA+OBkV67e47p53sHvvEH3hChG7z5cw6dqvEuaryDozEgQmAekuWYBMRKKmHuXy/WVSBE8M0yFWQxCpq0bAGR2iOYDVToxAqILeNfQRYhzjLE10GsyspgyCuMlVMtvExUzmlN7kikMk+VyEwIVOtKZoVSEP3RQu4sG4cyJJ5qE4ekbt/r38/J87z3Fhkabj2NY+4GNiXs82741j/FHvHFKCikCuTd5A7u/Daqm2WloQxQEgFu9x52DYHXzvukri7UspeNPc/YLElWysDIkFdyFDI3PPSMxyFBskPgtR8vGCILyyazDtOpU6ZgqMXsPccTXecGp+JU1yKd6Y4E+c+X7G6fb3Ke0OvnAgRe/J5/YQGv0+q81N1uwGA2e512tw9CzEV83jktUrtBJhZnctjberBKC2EtB7TEYgQXQcd+IzqZ9TXWYGGexbh68hhqqABiadkCcZau4g+xMnUzKWWFOCRv7fHVrKRC9VKu4UhuvcjK+01F8u3jrBXmmFStGjIrkUS5ilwFRch4nT/kXJmlANJNFYkXBap258nNiATz7P9qMcLSvFFveY/v9PZSjvjiHdL5Dwr1LGXFvDPsoOwp8cspcZ7cR877Oq+joZRvrNCKG/ZJf0uapa7036j0jeVdN/2jUlB8rby7KtS5rDSUQcFEAEhVhVTv2ko6gMrlDIwNZF3Fc4co1+6BgV5rlkUj7SKf+CAw0ts7MlJkzt4XZ3LX2cD00Nhz1tPX81DXdOIRG3tOSz73i+PA+ItubI91pPbi9CZ8Cr0jgcbGRjr6PK6mIdX60NjjsB2i/WH8ceCTJ00rUxWtPve/rh5FAyvYHnvk5AOVXE2WpAqN2q12bwiDkQY9hhMP1KwT8vsBtBggukjr6T/zdIZDAmiRztiWr+hkIMr1KHFAI500cmOBSEJyRtZBp96qQ8vIRw7m+u8zTQnqP/P8h0D1sbgiTWNcza0XuXYxWPxeVJhbKZ5uGZALJD5kmjEycv7zdclFnk/ltfx7ypeGqTn2oRxDy8qbhejFcw/rQsf3hq+LHN9bayyy4oaLKthB+3nxL6fE4cXbNxTtWx5e6NNC3yr5njfvPJv60QutW2LPJDZM3NisSldWGsoACiQCZMT/KyK3o89lsySYlcE73eRSi1cmru45QwDweTnt8WQEEIwP2M93udSak6muPvf5wnN6TuP1JbeGI2d0PijKhd3tS9Xl7uhzSN6hkfGRoUTlikRNTYeERVnqbE+jqpbY3SgBjarONOENEufQcaimdf9iS7X3+28TA5bcdqvdu4au7WmVnqGedslisfV/rg4g5O33rqi7MhpoMNFYx+SfWq3Oi8gQZa6+57Q0X68FEAR0ItrY64GjWOrRC2p1CakK1u87c+a4Ihrjc9dqREQNYHzc8/S9AlDdsD/jUq+yJCTuQqI+LVBw5Yq4rgrZmiM5pAsbovTA8N2auOTB+c7G2JU3c7wexq0UrNrjKFEwbCFccY+4Ccq4ZSgrDWWo6OhzkF1JasP4f9Xij7kpUf6p0JCcktl09CVLWmdUss47Z6Kx47E0Zme1TGW+4pppz5BSYiwprYGOx2y4ne70Cpz5vpOM2tvjA87pT53QAVTC3OXgZIDJtXsXYshB0CM5XDYLra1ELjyn0jPMc+mn0a6HTES5nnmPX1hgKshsFMLG9na1cdJpdXq5vg4gy0REDu8LTU7gmycqI2q55Pw/3liF2KXBwcsqyWPl73yggvUYCAsvWJ97RwbtCuuqppArK2SZeDgjUDCZ3Lh8lSIVrQphU0JjN3ywpSVKgHw0u0J3fiyUUFDtfYoNDeZFXPU7uEoZxXF7o0HLSkMZSXQ81nX+2ezzMj3+v0gt6S0OSXgYnN4inQIz06k3Sf0iVcbSWqyKdqYSk/ZB40NdljyqDADBzFjJwEiv1Xra+gcveS1dH9aD6dTjWlhQ1rwyUaMDvRppODPH5CRAi4nWY+ggtLzo7re7laxDfS1aTJ2tUHuPFiC0jNj0ielLmOsAlbdRk7z8Rrh6k0oRIA7xVWh57zogGD7VlvDcL/71hajKv6Qiij8h/GWNucSkg1y+hN3Adk649B0KR34nqQGUlCgB8PbKxn0AapoKKJK5h3WROLjir0u5zW+qsRRBUrx9m1EOxTeQbClllW1mZhZCKT9Qkclz4xyzPtpwns3+LkW2Wkh7yzs2qz1LY9vsN1mqkl7G3YDGnme6ep8daepKa0t6IhLYWG8occj4gNWJw+PpU8tLFt5WUzPJTwMz09Cpbvesp0ed6dmRh3KLSG0PXudAZ9qDhPDfIJEK+KEGganLbwKmU2d0w4NarVIF2w5UmapXbx64FvTrwkSASIjF9m7LxBvLLMwuLUJwbBIq30vkSsavvPNRE+eCALKMCbNW5/dHEAXiMmId1QI1N7keQ9ASnFJ384M3KtEoJbM1+8TYDUXP0DYYorOrRqoWCCnpkTF/KhxST2WYNZAFDut5dzX1rCLE4Wgdby/u6He4o4inH54rWy4RsTGWZwJ0lKA3FGrJbc8I1hDyNG6IEgl/NvxoZy+mWc+S9zG3hu3oDaX7YjacqviuSv9j2OxCWx61Kb1wa38VZUtDGelo7Hmm67zTrYYN0NGZjCvISEUsgpKHBGamMy76RWwS6XO+qG5uE/vxnX9NSbUc8kqdHaWmmGYFh1oaqLxq8p33H2nk0E/7p2S49sL0Pk4c/8hRPaG0ILvV4Aor/htwbSnRtDbx781m3yzxFGXhWlpM4MS5YCICIU7Q9FvferSGhIyML7KwwHXlNp3+Tzu+plyxNUJCYwCisyGILxATMjsLWkAOE0a1SSgag5D4vzggb11jKK081I7i2l+XbGAoDbqTNov6IzQXsjQUCjLPDZHbTs8NF81tJMfmX+RyWWieQusW38mGHyU7kE+cl/IN5N1MobFFrBcb/jSb/b2K7GHDjZXSeQsLbeGb2Wz/sqWhjEw09jxjO293q+86+hxjVrvVDVhsrr5GFLO+22nF4WrKO0GeIQUX6rVbrQAWSVLiF8z5+3b0uWZ67Va30jN7GZAcnoJmhrSOksPTQWAGxYqQuf/5kV5rUlnq9wF/NuNyYB8Y9/Spuzpx6kTwh6OjQQBzA8c+eeL8C5f8l//iyjr1OuarT1QvXspvABfwe6MGuLmc92OAOAhQoaEqPPh7P0y166oRVtTaDXIMakysBDUyGggDAlGg1namzT2YcPEYjUQXQUCQqbB0V06PrlREkzqKYsBQICf/b3sotTzU7YS4AdFD5Fzi56/OHyBT5MwlcW8r3qdECGlXwEIStEhL+vDiA0vc7Y481KbmyXqEvE+32V1t+SmK/LJb2EPpfyRZT136EpvFpp9CLge/lnFXITDSa5857cA5lnA+KC3qm7/6knU4CGA65Xn+84wP9M489sRkb/8FxRpuqm+5OT/b8fTxV58LHDcHJ/1CDXU6Gg8yfaP78PXRaR0fbbdMXPKZlgimRT4aKgklWIRMnVLwde++qmQZyAzoqogoZgAdv/6o5B31EkWrIxpBq6W6lqVria56kbW4Fvilp3/58nPqDg+fsekH/2aaaAVinIqERK8UWCv0T11AgAqo1LJSutF/IylcKnQf0EX+c/d8DUmYG2oCs8ulHHfHHZ4/UfSGBx98MNn6k5/8uPiwnVIaKBABsNnhO7WZ24Iino5b/3Q7+MvuNRR/tLzaQdk9UcZdiYIppsPLmG39NjPLwwPj0PFY17mv9f9nDZYGJIfLpp2fWSXkfe5ChOCkH9BEWFhg8gqh4OiVKNEwHDzEMkthdGlcSaE03sHgmJdoQmPQUgvGOgx1aqWpyCr1rfcAcgTvsFeR+dEIlQYqYywtgpZaDYYaCKtCOzqZ0BiAdwfP1dQpNI7xNBvAmoxYg3Tq/vTHFQAtsE7qmAAAIABJREFUWhlZJi5vRmOgqMYgoGVfqfOkNAZN6UTR4qY4pQHwl6YxAKu3wd1SRhm3A1tQhspKQxl3KQqlmH7O9a0eE1qaW71j49DY0/85c+KfSWPTMfZr0LaeadNhOuWyWfhgMwZobUVy9LfpIM7E8AQhwhAJA9IZW0OG01/gWCsW2+Nq5aYo8V8+vrBIiLUqDaJSCOrGewiIGtIZGtdCxCoAag+zFCO0DJUilVSCthrJ4XHZTEqQQnBSYxQgDqL+QGJRAeLLeIcvIqSEsywgRLOdC/nlcU5jhoQXUrmUgggyMc1NpUUQKEIAlYlY6ZaLeL6u9Tt0G6zK624pJcS99Hi6UoIWNzVh7uu8YXq3zLJcSmx/kbGF3pYYu7eFEMXiU5We1bLhVFvovKmvbld3RVlpKOMuRuEUU7vbx+SkGpvZ2POkJYRvFq/T6vRyf3d79PLghQjwxo98LLdIIYvtdx+VvEMvz0aoBBPQegSo1gLeQfcsLR0WMGhBi17DlUl87uEV835A0PMx66cPAIuLqzHiEI+zeBVk4jFirf02CxoQwGTiod8wE2PpKiglKdbi6A+HNEQjLF/+A7s7qMQnaFuf/mwzNQLEw9epNugBZBBBW42pEv3HnzYr8jWrppQGIB6/x5KmraiCOI5GmyH70yW8prIyeZLICmV1onqkLO9I1ET6VgpifvMLGfJEdWn/pBDpRzLPLfd/pfTJ6rzhVEW6ZX2a3q3I2Kwh23+0IpPkbSnly8mdNnkbLiTe8s6wYUuJP1whbOqJ0vdQHHk3vNmvrvgPVMoXWOTZy0pDGXcvGnue6Tr/7EgGabDkUHmgJEeS0qn/ghYDKncTx7/ueAAgODwo21xnf7fP0ey2O734JvywhuWEZOHQ513dtSvJC+vUj1Za0IrUiYSjALp7DzbX3QDkMJcuH2zW5k9AiE32u33ENMgQDOINPHEyQdkYRocW5F870wQxJoYV5uqqpGRtrFL+uxIKIya8FawQDBPyPudPPxB01CukDzGFC+E9X1o0RrJjLFpQ+ldVrBU3EpQuzYspBhvNsnmXBaE8ORjyHcDttKFIK2WGvK93Y63toFA84NbiBHOx20+Xm4lQfLlbEDxR+k+fF2WloYy7GQVSTBt7zjpwqkUxZ6aR+jxDCQ7s6ZlAx9cztIoEb6TSeLav76ynr6Pxi3/UbQRaWyWHx+N5/tPmhRiG/bRLrRYsD9cwufypEzpMpxwnhl3vCRw45bJZoMGMjnaHQ6LBgrlNyduIUS25PB6HNDkUOZzce4QoVPzz4NHH23SYTp2xgNHcZJCJLdJzWrocEYQqWkwW25n2uIhQdX/3b7QZkTpNaA/UVQkggojkcEgR4/6PHAWEg61xMSOlSofZrAEQRfSdST6lNA1HSzXcXKG+AYPqkKC6lvrEJEKiEVJSXUgqBzlyXga0JUv/rR2whx4/WGyg7qEihB8bZq/dQbi92sD7A8X/HnZKs9na6hTWD7bz05eVhjLubjT2PJNGTN3R52hW+CydqaIUz9imnQrz5LMzzQlq6xKwv44W0+Tkq3/zMgRWMGnwzzLhnZyTuubeDbaf7jleQ3DYOdcaDIkEh+3nl5G+5JB0TDidXmbnJEf/t1w2Cy0m9nmfHQmYmyy+uUraJUWVsEiSmei81PmJ1gaWhwd9sOD3R0Rkf7/VeeX4/ooKOPrbXef+coI4Mrxx75e+JHkvLxC9vrgqI2hgnUvPO38iLvh/9jYgX5skBnE0iVMoUuVfEgUB4nHCY3+dSChN8/nHUBrnZ1lVSmDLrCwxn7jBK+eSIGcUt065LPJyXUfVfhufspmHXmL6EzZT0VFzL1xLDKzO455YebFQIZNdlQHvJ3XkfYlcAbydv4ft62olrp6lH2z/z6zM01DGXYbGnrOenIae5LuOPo+nL3dEqoPyYfYkeWeGo096vjYz8FoA/8y9Jw77q5dp/pIDp/OnRh6A/XVIX/E8NtP735Z99V39Xef7Z+Zfmw4hOVxNQ/Zz3mdHHnsGOPqkA+eQMuGhD0veH9d2W/xLp0/j/G6b2dJkbmw6xqFP9n/ufP/MyY7xQW9Eg1E3957x4H45SH3Px2vdw2FYvehz252AjuqWe1em3lHIH5bjsE502dCgDa3oWb1JHGQNOpFIGFa5WcArIJDGXS2gk4msCVpBjsqpHtqmxujVAOnCXcBQSSicRhehQS7A05S1sLChg0IjEJPhkjtYvF8KK/mWvnQZdqNmVRHffIlEC7u6h70JWc6/50Ltu72ZXZptp56l+Dw78mdWtjSUUUYC4wNFy1hsbXhjE+dfGxmbvrdWbTA3WWhMM4D7Z3w1H+nG3e/24f1buiS1/cSpZrddKUTuxJEgyT7e52geHfXhdTqnbU+2an0zfqX4ljK8tlmHVs+xAyz7V9Zh+nmVdgLd/W1m6VQrRFiZegdAC7X7VucJyUDoepTQTRrqQSYeVVI/gNR9X1uTccdQz2xBtS5EANI0BkBm/cZy5rVEFGVCUQSIK7YHsTpP6YhCnH75m9MQ2/QhWJ1nrX3Pf36z05SA3bZU35Y9bNM7XvoqRcL6Nlz3NppwsvZWuj1ga7g1f2NlpaGMMhLo6PM4mt0FjdNbHN7R2ex2T3ed/OJZV1cNikHiDx+VvEOvPXTW09cxPua1dJ384lklJOLJnp6+RMGL433JkMx0jsJkdfKzPW0PdVm8Y+M09pz19LfpMH385GGRaIj36o61NOoqgESBCXF//WzD1CuTIFDferwa5CjUf+RIQkbHAMR3l2ozEj3TJHh0mVj6e+WEk4vJ8vX1hiwHQhxIFNWUgfUUhaZAQXWhCLSmTVfzTkH3+Ie0KV9MAoX5LW+NjEzHjgi8IpNsf/49Hhixq3K0lL+H0vWG3Vh9N1B2T5RRRho6+jzbMUznHW5uskBTY0bhrkxy6yQH9vTzVqtTcngeU9+P/4Xbh6mwtbyx5xnptMrFXVvF0s/feAiAmf+879F7Ki4uzwfW0eqJhYlf8/qvIYgIcULCfZVcBrj6IyUGVPzQ06ZfPPeuHK8QluJpqRMaDVUyN9NTI5KnkwZtnGi6e0JASNchRDQ3QuSwQClehgzvRs7b0hENpqp5t5+qf314M3mX8gsX8mkI40W8E7t0Ouc1tu+IMbl4xYHtz7+zuC3ek+0senu/tM2uviM+ndIsDW+dfVjIxVOvbHHRMspIIDDSa82oHzU+YLXmKR91C7aRuWj+KlabGZ6kpm7sOevp62B8QPE1qB07+hyKI8LrtPaOBAi8dr75TFcNFgszF14778PrtFqdE+g4elyZU5lnfMCqzKOMOvdTsFgkm+vMvlWYHBxdRQD2P3CyizCsx6nZTwUIlRh1lRVxZFj7j5dVAmtZjxZRIP7mc+/IiHrimUI0Lqfu3UIWR1OMlDNC6ZauMeh1xBEOvrNORoqEBiFlZlDnzXyb/WZT2KR/Isb97VU5QjX8rT/fhZzLDe+FpV/Zd8T8sBdUhCxsyiqw5S8ha+BecBvtFEq0fGzTMlSK0vDW2Se+Ovbll+VsfPszW1+3jDKSsFhIVrgcH5u2WIr2vp3YhooTGBnySg6PR83cDIz0WoeaXEqupuf0jN1qnznd9xEgGvC6+//+QSWHU5Kkaiac1q++DDD+DavV6vSqZBLKqHcawefzuu3/z5wOWh9vMwDI1/qffenibJyozOIscZAj6OJr8ayDcT1MNFW/KqbYGNJTyuOE42qDnOGJENCALmXbzz6EwhEgcg3FH5FELFcj2PD4Kn6SVx5O6iQTw0vFeuZC5uLEscrs1oPf/eLOFllPLpfPhLBZbFPIbUdm5JXTtyVioPQvoVDs5IYDdxC30o9wC7xFpSgNVy6N8eXusoZQxi6huRmleDUEZmhuTvtofEDJdcwQzMnGZNRhTosi3XNG7gQKqzg5KsVlVaUYH7CmWweA8RfdzY5neFbpfeGVVzGYvGPjEGV2nWrD4WvfsFqtdrf3Z29HMNQz+7cjgfEB5yXaHR6Pq/unTquqPjAxGYIGqb318FoEqqcWtegFxHWWKxHjIFCrQSeAzPw6KEZoAVGDSKbAFjmmkEDICgelCGB8vE2X92uQiUEk+2qffQwfbsgdmXtUa4rXekhLx8iDtXe3VzbrYji75drv7ZKha7MSa0eETZFJNjV/3s3fEdf0vRB1kdzDLdaxdmO5UpSGYyc6d3LJMsrIQmdns0J+EHhtpqkzVbF6fMA5bXN5PB6XbTph1Q+M9DpxeDwej8fR7H52JJCnZXzAfr7LpRIuTeepS7UdFFZxgpMBwOu0Wq0DbwBzP79i1HHxz3qcXuqVLgafygLhxTt07ob6lP2+dUJBvE67ew5kVkI/9/60vrvNALNTK4Tm0VSef/FFL6GjNUNWq310AahCcnjOWEDAJHgnJq8CTEy8vcq6TFxmdpY1ELSEY0RkUGz3Cd9APMZ6FoVSnCvvqi9jIGvjIhC5cCGSSKsUqM66lQvZQVFy5qe8O5v17ZkbcrgvRWKZPpEigVY1uadfGpmmXrdFtqcsXP3HLbknduOA3hGBlztJcqt7QaAmcVsi+27lordYx9ql5UpRGu7r/fqXv/NIOYKhjF2DuWl6bBwCr52nyZxsHR/zWroeUgiWTkvesXEg8Np5n9SphKl19HnO9jTmtiT+A9DY1LzTmy2g4sy91O+rQZIki63fNj34oxWWudcI89SB9EmJ2ipQgvakThPVLdHRN5ZZHuv909dZUNIOWx9viRFdB+JE5kf/o+aMq9tUiQiRSuYqgbe9PnU5YR0IrCyATHAetKbuM60gElb4FxCr0AvIEdLqa6ZkqlhFs6Eg8bIAQlRYB1YumxNESbKMrM08emSxWCR1vhPYP0t2Kc14dv5CAdYGkMlTpzKt4FY4krOm0CRtml26pvM3N++euGXyYPtCbgtb3dUnSle2SlRiSv8SsjS5vAP3lOa091GK0vDKU498B77zSDkQsoxdQuNDXcwE8M80d2aGrasXc6vV6VXLR4ElTa+gQEvKY+H05m/fht8iv4pz6FHXZ+sUlSLa1AwxVg611UH9oRqL7RPXXmdVpF2yWGz9tukxMB29l8UVJuZOu4dcNgvtkoVDdSLIYDqoQ0BPcOzFf1mMIArEFjhUTS3a/QkpLeuBxmAMQIhDNDg6OAnxuJZ9IuhYXyUsAwig73xaKXcpJ/SG+KrgW4L1xO4FRDGlUsigHKLCvnZ/GlHSakg9WEU1KDKRaSHWVOUTzSIqkUNxobN5eoWSIc94i9Xwzot4nj+nHUERUZcu20rhBr69Qi5d3GY1btbcsmVlq5QvIe/kt/3bu9NRSsrlZ74ty9/e9Z2UcTdDYUBqnm56DGbS2rMoCiAAvhk/ZFwFs1vGB6xOHB5Pn5rFkN43D+MjikHi/EyAjvR58+kioKg4rwVgprmzh5mh5PBg0zJepxPwgtmgq/ngPbyqDHmPUPvDlrnaWuYwNTVTs+K79DY3w7Q/2gEBlpnw+mDwCggCMUmqHD4XjnLRu2yoJX6D8GrXY795yPv6hJA865bwOq3KS7n6cVvNC27FFRDlJrCORiBWgSwgxwiPPafGOKYS6+Tk/wkgy+nFplO95BsTeb+CROykRrULxJdXs3oIiW4yMggVEAcNVTFWs7rdstO7slJYWythtfCb2X9gO4dSePrSX99e2bbZwItbufk7jtqSHcp4vO3LlcmdytgTUBmQMkoFJcpHAeMDaghho0pnBIlEx9yWwMw0UprJYnpmYxd1R6fkS+NlCow866arUOGiBMljSqno6JR87n73CpLD47K1n6jFHzN/+pHExzfem7bcW7viGx31+dx2p5ejH5eCQSr01MD4gN29ACaby9PfpkMD88PnFJ9CvaGqTmBVRnO8p7Hj66cMauFpXSUmPXWHwXj/AQGWX3DPmk1J376AFtY11LSYk7Z+nQ5y77dArtQWZFnAWDQ+QKd+ls+TkJw0/RYaV7tXH8y8p2TtR0xNUCoqqSvqf2g9knhVksYAVP/mFqg6bhfTTiEUufGXuNVCGsMOZl5sGXdEAGZe7Iado/hvvePLlUru9NbZh499dSz5tvObV17tvW8nN1LGXY4NGJAsNldfI0Bjz1nHgFWhM7LYXGc7oCO9pb9ryD5z0jbtVBskyZJrmsiDjj6P4xvqLOrUCcql8QHrUJPy9sYir48F6Otsdjp/aoTxh9SYho7HbA1e9yxep9ULaKjed/9HQL2nv3Xe55v3AaYGy8mvdJ3v/96w12QiGEz0h9YnexoJ1OqIKrWzIQLzoRtYfrVh6d9uVAFD/3RTra4QWSMIXNcYOj/e/OrFlYghjD8YBdVs0P5Bw8QvlgldSdXWikQABIvr89iHfMQEHSaNHFyp1hCTOd6+b3LsptpVBliIoBcJA3H2HWHtKhGoPLxv7d2boEZW5kfCelHR0ipMTcYybQnz1wpGLICanLm5E26NxaKfKxGim4Dw4S3Se+0FXUHBhjf+W8xiVGQ/u3r5vsU3+9uCrVl3tvO1CHIJg946+/Cxr7a+nCJmeOUp4ZHJst5Qxh5EOqvSjo9Ndsh9AYE/f0LJa8BkIhi0nPGc1Zx59Ln5JpNwNSjU8qv/xXZx0D0PgNjS//tH+7/1Ux74iqvpu/Yhv3pnN+gJhdHW0iRw9SZCVff/PWT+Px997vpDnhd/PzDwuN2boF3WVRLRtzr+qudvfrt/yniMa1fy7lhyPP6684VQWovZxHvBwsGGaUg/gjRF4hMzxqikkNnHl5hDDLlTSFNKtGKyQOa2kHKKPfjgg8nWH//4xzswdwGkH+Lbl3NZl84doZLcjjTakf3kXWWnJrxzsaExacMhuZ2L/wWWFAj5x18d+/LL6VROn/n2y18e++oflwMhy7hzUCLlA2N5+iW6pUgeH+qyTM+MqOSMQ0qa5+gC0GBzefpPWpBOPwIH7xFZuxoMo/vw523TqsZgkSRL/Gi0o8/12ToAtLS1GADJ4Rka+I0qiC4xtWiqjxM9dI9/4Ll3ZMJj1oHx4FKMSg0NDRggsgZLk//9D67VidRW59EYNJUAXucLIRB1YPrUfRoQ8AfTSkgI6PK5AxR7v3piVFWSqzEkhqNJDRcEkJFFNUgiA0Vl+WbTGzKQtlCuxqCpNpk2Pfs9W9I4t4d0M/IeidTbmsPl1mx+rzmDtoPdyNRNJ9LYcPJctq7iKMc0lHFXIEn54HHgVDWEXIIHwDut8DQ6kvEUibEum5nX00gffO6ZTo/LZqFlxW23/iMWoF2qcb843thz1tPHgNXav1ivRDk0Xvj780C7ZEE62TTtkzq1/T0K35Pd7WNiKgR4ndbTfyEfNmLUYJGOBgHfoPP19u4GDLV4nf0XIqzFmJ0lpPAhicQnn7sQYSnTAl/bfj8QS6RaaiEegeAP3oqliVjlRQU6kEFDRt6jKnwVcbuanrOZGi4ok8TQKl5OAZ2oFp2SAa0+z6i8EFI5HDuO2EowWFRf0dbk6kx3mhDaPb6gndIASkwbKb1lB/d2e7H94IwSo1JuMU/DZ772zc5MnoZXnnrkO53f/FqZJLKMOwTjY17ptBKj0NEp+c6/FshH+QCQ6GZuUmIh0ukitDS3JmIuwWJ7TBl88ITFYnukCSyWGg6pcZdpZJGB18778Pl8kuPrj3VZpt+7AdD2pcctlu5ui6W724JRw4H6dsnCiZqpo1/pt1Tg+9mHXB6PQ8IUmqOz+2MHaD0CGswmTHpEYnIV5iq0Bkx6ao9gNmCoV4tvL01MmQ1KrqOxJVW0sX5f7vcSV5kjYgLaLNIlEV1C3AoC1NZlDU2eVRHFDiGzpnBEK1mgcg7RYiEUrZOZjt0IeIsu564e/MatLn2yDRTKKkxij4jVvDI+d/OltNxZ2BGVrvgkm1WetvknUZKl4b7eV698czKNp6Ec0FDGHYXAzHTqjbnJknhZKKkyGz63XSGCnpwslovR3MzP5hIrJsgi3/4z+8xpxXTh7H2tpXlu9Nx1gMaHuqL/+q/Rrgf2R1kG8/0sRVmZU5kqTJ/oaSQwM011g2/pl042wdoalXqOPek6eZhDlcRW8YeIRqmp5UAYIKSRTlQigHDkv37yELUVyCxUO56+V+VUaPywEUAQ8tA2CxXo6jOb1hE0oEWnHDJLi0YjlnoqNQDmBigqxeWEP0PcuXN+C0ddZeeDm1+/9guluCeS5/juXfS3gL1J9XjXIq/Gs0tUFpvSFIt03nBsqe6J+3pfTa9VVdYYyriTkMEL6Z/xJV6qtoQNYWlAcrhsFiRHkmsyuvh9ezY/VOcH8TM3k0YWGWM22M6QdWC8o89lw93vDVWLFQCB1/7ev7CwEjHdWAY99QfxLTM3l67GpLZ9Y5ErQeIJO75Gq6uqBZDXmArim8W/im559GoaJaJGQNRzCIAKASobWurQaZHlNNpmvdQKeoEKkaXricaa7m4zOpm1GESJgKADU/cn6gjMsxYD8M+CIsWFmpZqRBB0tLfm1H5KMTpkQExeI4t/79vG2tiPNy82C9hI0g/6vGf3HtEbFOydzey2UrUHVbciKJ5Isk1kaYrFJy+iVhbXOMsxDWXcDUinfEh4G3IJHgqNbfctsgzAJWcqYjIf7jGsE0sni9TQYJpACZ9UdQBtPMQycCOGQOjv7D8MUVMJx9vqFlg+kcVUkVoaiK2oMRD+EIDZkBZCGCcSIiQjgHy13+1jPkY8jNf5D2FFcq/90ws+Ioq6oK9WR4W9kw26sEw8QopuevnvRv0JsudKqgTkCARHR31Ko6at32WzJCo+VMTfiQDIESYm80U/5EM8cYjJIG4rDDIFYQsqiEiLKbc19lqRn3iviaU9bksockvOG8OfK+eKS77cn2Ov/UDFsdvOlyLiv7iaVeSjIjwNrzwlPMLL8rc/88pTwiPfydPhyy+Xq2OXsReRJD8A1AS6NMoHyeHpKUD5MDKUb7qOL0i6Ca/TPgEG6ZmeNKk+95Ld6wOfzyApDaaDIjMX7XYvYFcEkhyuzNzP0mqUCWfvgmEJmUiI6xqq5/E6BwG8diuPtwGBAWti0ITTDqABORmjGFldwg+CFmpIZyqoENHAWir67/L1xIlRX8e80jO8kkh1gNm0WhBq+mJaqsRaGoFj4tML/fYLycZ4OPtinkPyKICoI5ZdcyIxQTz/qM1iK6PjTAVzW/U9u0QHuSG2lmyZ7HxnyctC2BFWiduFvKmzyZatYTfYJoqzO2yQbVEKT0MZZdylUGgYXE1Dyf9PsjgERp60u4Xu7urRpZO26cE3my0TnHY1/andHWtrP1RLNPgL/yW9iWCGWDJhrjf4L4mnztQPD05VY4yyeoDaJRY0mCuZnW//mGXi7RVEuBIEMJuIBg9EuL4gQKViO281ManMKmqIayTXi594vqf/3xO+CW0N0Roz1/0aGX1cDCmKRqWGtXil9K3/iv2/e4nVm0zxYLAgL9LxKi5nMkNr0MSIZUt3USsSjRdJUBDYt0+4eUPOaBLltBzMBLVDWoNxn7CwLO8Ws0MK4hnP3y6esr6QaSGp8Xj+Rn2ZztPwk5+oPA258iDZvh0UkTGly4ziPW8901EpD5X70Zbn385UW0Ypulrev5lCjaWsVahbiT9x8S9/B3gansotTpWvrYwy7ibMrGA48cB+oL7ntDTx9grATAxt6MJc12O/eexGfTV8QLLY+m0WWlux2FwOKSg98UdDLtuBa784aKylgjoj9x0iUkVjNUfPeIY8X+9s5ujH268scKwV6Uz3YpAg1xcAMH1WyQ+tSOoh8RiEvXZrv1LAWqMBiC7DrJ8YsTghIS4otsa1mEYji7zxkpe4AJrY8s2MxxEFVBpngMur2adZTDVApB8j+y1HPt6YnTApgJ5KAwYBUUAmU2MAZOJCxtusz0Xtwo08GsPGR3Ny9zmBngWwfnWk94UcC8hysVJmhTSG3UPpFuziPW9LGkKJAXfbuYVv2LKrKPGbzJsvyiZ3u+EveGt+4lJppMso467ABqSQmY4G8Pa7AafVC0Yj1S/ZJ0MAPrfdqXR47XxL7Irbh+mU6+R5u9MHXqsXWkzMPXA/r19cvM7KJ/otl/svRPClJp8Qq/bNT968MjkK1J06c2B4cEomOGy1DgMgYG7g2G93jw+OrqRtJxaD1sdbfv6C30jkGpAQybL6acw7OAmIsDafJZXjMujiqcLVpZxmN3y+H6Te6QzEQ8RllPLc6vVdQFOBRiAcg4ZHqmdfXgHQ7zOGQwvIIOipXWMxbblUnGYG8l6dEdNiLZNPlH+CKg2rWSRV8XzzVuX/9ZXzfZdIeG6XwfcWbKDIzDuyaNlWvgUUd3kU14yLWRreOvuwkmCZpzD2I9/hy93lgIYy7iI09pz1qHBIWLrbDZhOKhkVHo/H892vSJPLaY0um9lSTUVsqua3bBZOHG9saqa1FcnhkDAuBKWTTcl0hbbPPAwGLA2YMLeZOdZAfPVm8uPF4Quftt2DgLYayeGSDFTWsHwdr6IxVJ/yePpbkrfsyRem4kQXMNRRJaDVpq7dog6qNJoqY4MMi6zmWv+TGkPLr+TlZsoTs6irTfaMhPI5FGRi8cRBo2oMQDjp+5DDGRrD5iAjlD42W2MA+Zzbl9svXDTWtdBONv8MWwuk38EcgTudAmEvY1McmlsYxbb/EoqnSBT5qJSYhmRE5Nb3V0YZdwayLA2pmIbzy/iU2tOSw4FzqOlp49BzF/QthKYAMEntwekP9Xed63cvKZEHBwxcD+mQvuZqGrKf85lrqv0Lre0LczXtvul7u5f/aXQ2ZKBqmVXlX6Aey35WZk2iLrgosqIIVW3GrVms43CL+eZl/1J6MYkEBNDto/N/73/7j/qn4gAaQc1T0IiI66zJagkIEeKZxSSKlYYQRYoFLRRC3shG7QExej2f42H7t8XccK4smNur/BPZFbyNRhYXslavdHmG1VDI3NoTOxJ5kNV/w1Gle+6L+8izdrhOoIZEAAAgAElEQVRTkXp3OXJ/9937njf8S7gFpqNS3BOf+Xb5r6mMuxozc/iSctrrdAI854OExgAEvRMw0Z92cb2+TsqUd+LUMe+wn4kJlNKXozpLuyV0ybcqo/+QFH7TSxhfGNBadKysaTD+1pkHRgdVd8U9koT3h+8Z9nPz5/6lVdBiPoBWxjcLepVZQIa1m3id/ckdxBL/bmNxYiCo0r/+qH5+KjP2LynH9RoqBFbS7fsJjWGToj1v34TGkBn8uCPHy4aHVK7GACws5LbFixVFLWTXLR6OnoXdu9bnSqn0zbxfD/Lb6+UpTnOpXNk3tcPijgOK/v3cgm+gNJ6GhKMiE+VAyDLuDjT2DXk8aa6J1tYU15PH4/F4XEqoo+TwOKRU46eN1WhQ/Bp9n+/zuGwWi83l8XhckoGaj3zhGdshDmgJv5keJrFacwhzZUyvS2gMAtXSf+vr626Jo69k3+FqQ2s7BqM/mLB8hDGd8nTq05iHwNJAi+lgdfI9ALIq/eenwvrsrMhEn3AFJiMmPZVi8kpRpzg4ZBAP5KE1AARNFk1CdVoZK/YdhAZTxjlXMnV0zkq5TZWlSWBN/tF5EBsrTiO9TbLFLQT875Ik2FlmoduFO8LJUvxvJp0yYe8/TilKw1tnn/gq37wiy1e+2cmXX5Zl+eUvQ2bdyzLKeJ8jUenS6eXQhyXv0LkbaqtK93Tow5J36M9/Nq00PvGE1f7GA/dljLf/szF6/rUAgdd+BjX7Abj3cNYy85M+/CHCwXaHx2WzUK9hxWu3Wgdlm+tJM9GwtrrtC6d06gVZ1/a0zcKJ40hNiRPJgAy+WaaC11ZAEKiswWyAhiqg+pgFPejC6FJLKjWrtMoBFcE3SzBMHOoMalNUBNBA/PqhU62JYYZjNYCABkQZOcNgsKKwRQLI3LwGs8GdEUp5ZlnLbav65V/NsaHG2n9ZLG0P5l2scpnXfVCKwM7qtlMyfpsK0C3GnUL7uClsVkvYWWfHFr7PUpSGK5fGaD1+H9x3vJXJy28ppbG/842zb21po2WUcUfB60woC6AUquR4n6N5dNSH1+mctiXonjIa//C7Hs8zqVBHoKPPceyC3+e2W+3u2UNnvtXTyMwck5WSRflcpCVhwDAbMEhqAYRoIqNRobzW6qOzbvuwmnNpMLYyGeD1sQBmGhoASLhR2h39bToqZISY6A/B7CpwwBjdL2Dah5ieaNj22QMy0czDJxZnUQ1QXCGOrpIYCPsnf57sEboi1IBMDGJpYQppJ5BSClMtfZkyPdRW55xSO5zFVf2hQ7kH4bWpEqtp5s+92AClSPTtS7sSZXzp6sUdJIa3dgW/9Q+4F+w3pTz1lk0am6ORPnaic+zSFfVN6lUZZbxPoWRM9LfpMJ1SnA5faIIrr3zF6W2XMkpRAHR0SgBaov/zxXHG/8LtJ0SywjYdfR6XzQJYbM88AgSXIpge6HvGZsZAy0HkNpdyx9VW87HORgLnvneVcFVidq/T6cU/tYKx23XGUg0QCrpHFnSE5vxATXOibIXuXgNcHJvwRYhDOJwU6boD92rrT3YznwxiEIFIpPHjhtQzV+U7QCJrAPINJicBvSLkQ8t5eirH5D5NmluEhD9CMT0sreQcpdl5Dds84+eHv58r+aeWSzzBV7aQPAFburXvaqLjhpvZWUv47dI/igjpUh5wN7a9HfvN9nWO3XZwlFQau/vLfGf0FRRTw3dGX4G3Lk/SeeLYzu+njDJuO1TTgoqBcUytDSwPK2+ffROdP/CQoj1kYXxs2uZytVfgn/U6rd+dM0BrplrR+FCXBbXQNiBzbdhqd/sJMX/Iklk+KzDy7I+PH+bhXjV4QomWcNksJuOo84dVBovt6TYdLb/edQjpUfPY67z7tl8JmXA8/E4Iwt6X16tSRSZrASIXR32+0R+faMdopMWE2RBHh/7y4OjhdqOOY60AqzJUPXgAxNanW0QEQMRQi1FL7RFMLVgawqqQ14A+v5XgZowsj4XyvELWmVbgJ8gat0PlKXIhGA7kTn7zL25Naew7xSlQCrIE1ZYl8dYGbllI74UAglwtYcNkyLyvN4XtPGyJNNJvnX342IuPXXm1975EIYrOcnHsMu4WpPIwc7ifAiO9djXXX3J4+jqSnTvHVIeGxeY629M4PqC+VXsRGOnt//7V4EKaYb/d4fl6R2K5ByXvtU5PXwfj37A6J0hbITDyFfv3orY/7r/xB/bRxjO25cGZ00//4g+fu1afIHQCQNf22YP//v13ZCo/2q3999GQvobwMlhsZ2rcg5Ppj6cRqf/c0+LIczc+7Tr8T/YrJedXlphPoROI5PbbaHCxLNAMVIn5KCfyTFiniS/mUDXkgcHlGSqYcrlNbJY2eJurbIdLeGsr5qL0aXecc3rDUe/LpNNSHmo7D16ie+K+3lfVctif+Xa5OHYZ7yMUq2+Z2zWLLXJ8wP79RSVdwmWbdg6Mqy1AR19aKe3xAee0zeXxeDwOUnUygwsoXg+XzYJJz4RTtWwAcz/3Sp0df/Ulq6IxmE4pjg0g8Bf/r59Q0G13XkyZ4KcWZFVjMBugDiBy4ftBGRHkmdG4BuLLgHgI9+BkVr5BLE5w+Ll3ZELnStMY6o4q/1VOmnziIhFmKQD5NIYSULLqspovVGFf7q5K0xiA0Iu7Z2nIvUHu+AW39NvzXvC+KyhuXdiyhNvZB7xT4j92+2ctl8Yuo4wcJDWJwEiv1Wq1u314ncp/hl5O69DR5/nudxUtQql7PT5Gu3GZLF//+JhXOq04KTo6Jd/5ROllY/Kf3wrB6jYzFpvLgdM5GWV51tI0/43hBdodLpuF4LC6B6vVPhFr6fe4bBb/bCLmce6llxHQf0jCfGIplIqFjIMgUnGtXhsHvR5YNzwj6cmqYC1klmrQAOKBlNNBAKqU2EVBUQYWr6Z6a4/qlVNUENNO00SYZaEjS8zzUcHDuPgprcuziF764DZOtl/ZxeyJvYadyp7YApVhKkV4N8XwTpn694Ijo3TkPnXWd74dxaLEqOWUe0J5/dXWMkNkGe8vBEZ6n51pxuv1SQ5P31nP+IDV6gQwnXKd/LHdTXc3b7w15xu0WgcBA5Iy6km7e1adop2huaYnmnUT0/MZM89MQ6f6xtxkSf9s+R8S5SSOfMbxW0N2+5DU7v/5BVZjPt+gj2PSnNOuOCd0xurIwgpAfKrfalfHv/WjmcMAUZnYm17gJhDjnjbT6qXgjbBC6zQfk+FmGJB9X3+7Gl0FkbSLvJweNGh6UAz+OBa/nrqVy8CqEruY6KmeMgLI0bdVyiQ5vomzJ59xoODw4vNm1ptSnB5h73Y8CT8ZGOeW6g3KUX5H28YLydHiHoEiLFi37Nu4lV/77SKhyvudb3kPJenjrzx17KutLyccEvf1vnrlm5OPlLmdyni/wefltMfj6esIjDxpdV6yuZRsh2HnZBTp9Mn9+Ca1+zFgsT1uCfH2Pw5YrXb3LLQ6FBfD3M9ofrPfG8I3mHR5BEZ6n51JBlcOjPtnlAgIZuZYiBJaQnL0t+kwLDmf/Z8+yXH2g9eI3/+0zWI+sR9mvM0Ol81CrUgk/JCyHz26tn6XzUKLHsKTXh+TNCRlvBLW996F4HIUWUDUYxBUU/8BLYba+dnVpMag1ycGWM48fa9ysAR/vKYcMiKigDatBIVApvw+/CtVbBW7lzSwncHbuUBuynZdIgP01rAXnA7bTB/IGpj1RHeKmyALd5atoghKKo09ml2d6r7erycSKsoo4/YiMNJrtfZupcZQLqTODhgfsNq/t4jp42rWw6FWvy/hb7Bob1h+oxv3v6xpmZqu7bZgacA0OfTnf+50+5gJRb1z3d0WTLTOOZ2Tyr182ef1GbBI7WDyPj/ktTS/abdaf88boVaD5XHbtPPlqI7YKisftU07B+ariVyYWG32V5jQwzIQZSUO+4810th0iHVNRBMFmFqHljMej6N1clYAfafH42hNWhBicZBZ16gGfNHSLUcRtYRkdPuULuEwauzA9Lefe0dG1GGora9WTul14jLRMAAi1ag5k6lcg3d/kl5gs1QIGOqKJUMIu5YpsTG2LGG3IA92j1LpzhJOJcYrJLWQHXm6Lagde0EV2yMoxzSUcWdj/EV3s8PR7E4PXlNoGscHNohwHB+w9o4EZ+a4dDm9dcwrPX48rWFykoUFvE6724evwXH2i198xsY7UQwf/eIXn+lenCWIb3R08ZhJjK348Y2O+ggyiapqvHF+llbH0KkV7wQEmb1+xPf6RPspaaUywlLM+JG6GR8T/xEiHGHxZs8ztp8NT+raHq8Z9jJ5hXCYd+YhyjoYgr8YB3NNddIeXxeBqUGr1ankQmj2MT42mVWdUg4RWgOI+0YXYOkalRoiN9N6aNAnjsF4hNBKXa1ylqYfjHHWEpaG3PhEQZckbshAKsYh/XCWCS0Wi3GUc8tZ7XnBV0ae6M4kKXKWeC4icW+NMM6bHVoK7iz2zHTscEBoKSmXOVEMrzwlPDJZzrksYw8hEAg0NhYsM1RkXCIlIj03IjBw2n7pM57nPx8Y6bVfqGG5zXW2h7QOF/p7+gOPqh3O3+DQGdeHhpyTK37Nk56vK9aKoZ9Zur7Sdb7//DKHfskywWlPn3mk1z5z2oFTZZdMwmJ7Wvjr55TSlCY9wcOt+CYhlZeorcGwxsKHHI5DzsEfMR8F0NUQyUevlIYqyFOlKRsadHJGlMMtgCDm0Q9uGfR6xcySF2aP51vqyxJTLreTwLahn3uzjvDbm0a4qdzLLWx1+09XevjFjuN2xTTsLEqyNNzX+6r8Mo+kSlWVNYYy9gzURIZGXutNsjFlJ1Im6kakUhqTY+0/yjayB0Z67W9rCP5wJABE+cUkPrfdarV/f5Hp55VJ+i9ECP7DSIDgZADfPBNOu9vnvxBkwmntHQlgRu+b9f3ovZ7Tx3yzrHSedeBUF56fmU4uZVCrW53tOVgnojcBBMPgm2x3eDyu9koZzJ9q03FAy0K9ydI2M+atnxeo1QCZGoN4ot/jOVWf6ZcX7/2UhcrkasYCX2FsY43BUKdkVdTnd/yL+jxeBaEYKdPuagziBhHehTUGwJ/fQFXkSrqdm1zx++vWHB9b3syeQl4zwB39dHeErWJD60vJnO+f+bYsf3tH9lRGGbuBxp6znh4YH+idCWBOtQdGeu3uZofH06G8G/EXmiEdlbQectutbsBw0vP//S6KdeFCROFqutDf0x/4dE8jFwBDHSc+aqnpPNvHgHWo6ZmexsDIz/RQqf87q9PfUsekc+BRjwOr/Xw9c4P/fNxM80Pt3pcmjh3G67R6AcwmLeHwKY/nkyNfsb/gZ8JptWK2GPD5f3BBR+0ypo6TuN1eE7U3WIoA1B7hwBK+JQDil/qt1lSdbAXx//yBDxIGi1BaGWhBoCKfrwGyWZd0IrpqVheJA/H5fCMgHs4zVYH58y+TBU0utTQIHzkmvnGlFLaFeImUDHlRVyx1olCmw56SBHtqM0UgF64ZXSTJYptPlxUYUUY6SsltKcc0lPH+gMKokKwrlWp+1t3sSBEyNfb0ZEmEo0+6moasA+ONPWfTeZtYnqW1FaORQ/+uWCfa+s8mCJYwtTZy4rj6oqGSms6zfR3Q0edodtutVrt79qMOz1/90bc8Htf9AgbpsQ46HrOZ525Sc+pb/U9IHP+CpOPKFQCD5PJ4nmwUMAjDA+OgpbGB1lYstie7DtFiomqdpSjBc24ftB5iKeFwWLrK3AocOSOlKkekNAajEenMp5QEBxnQQIPN5fE42kE5eKsO3qulukpn7O4/JoJGZWRSAh5VCETihG4Sz3vRFVCqXBZBkeM5z3zJ2pt5hb5cmsZQ4vIFsfiNDcmdbpfU2Q5D863BZlMob8vlO3fRO0XN2hp29m+mVKXhrbMPC2l4uFzhsoy9BYVcCRLlIAEYH0hyIqlIS7NI+iaCkwH+/a8yPj36pKurhsAvMP6vrq6a5BotRp3vR2+gmDWSGsbBE5bpmYAy41CTSylslfg0eHWZhg82woXnX/ATsZz8JIGZps4Omg6ptSRC+MF0pIbQoqXJDLB4Hf8NfO5+tw9/HDFh5dcLSr0oFYb2TxGFq4PeEOw75fF4HJL6kfaX+z9bx6W//MG6gSN1AMRg/aFG4D21z3rdfQtR1lYjC6P9V+IQS2M8kHNeKOdq1rEjJ80J2Y4IQUAU0CSC4fJAVufWpFFLyZF8PbeDrcgC/Rd2lqRhO0d2EWG2l/WGLHmcN0xyL+z/jvAXbBM7nk1TknsiEQj5alog5LGHKYc1lLGH0NHn8fQl3gRm1DaH5B1qcj3Ds1kE0JmQWb4K4HPbrTNnbDD3kt3rA1gYtE8BTisOT+dYv2+dBZXdSSkqATDh9YHiygCeHXkofbn3FiLU1KsvYM794nhPX08jF14JcHks0NfZzMzlAet33zZA64MzdrtqKXn3Uy5P1/M9/W9c0+qqo+hpqWXqGsI6cs0Ha0O/WILQxA/274MIrMHNYev/MsyB1nreFjU3r/9Hv7odgavikQPC1SWIX7NbramH1i/9WzJIMsNXIJAoap0jc/OdrkriBaSVi9DUEFuO55k6haTtM1agErU6bn9/t9j/0sIOMDuUHHoZ/r/G+R87pTfkPbI3JaXuUHN6iVEa72+BfcehlD+2UrInXnlKeIQsBsh8bWWUcRuQWxGC3KpRGZAcnr6OZAWpdJhaYGr/DW6sgtEIIYz/m+vXztnP+Qgm+hx43POXp1/utQ5eP8LSVQAR6fc9fR2Bb/TYJ4ySJZhIlPjRCssQDAKYqA5qV4gmYg6q0awQa3W42gbt7kSggF5yvfiJ53v6L0RAg+X0I4vulxdzz9UsMayvazi8OOvLfpbqOkw6pmd3kkhJCykRr2yj5LpShSBWsb6aucm8AQ27AQHpDx75F+fLmV+RzuUZyVOw6ic/SWVPbLkI0xZmKDLJZufZCyg9/eG25xrsxgZu/UNtNt9kw/zYckxDGe9jdPQpFaX723RgUktGSd6hkUBgZpoWEy0m9VPTKY/H43nyKEFu6OuRHGeMC6wKzP4iODMHpzwej8dxP4C2DnjkGcmwdLPb5XFJeuK8MRMAPxEDJz7YpfoqoswGCdbUG41IZ2xao7a2GsTwPQ0WW79kWCEmoJ/8x2+NzAu6WnN3uwGTed37x0NTCxEAMWbyvXktLiJXgA6jRi0qLWjQykBajenoovCgx2Wrz/IQVP56f/ONnBrTiVQKAQQaK9M/K+E2m2EUUKZWTQt5kydKQnz1/gNZXbeqMaTbTXUFe6VBxputMbDxxnfkuN8+g2Rynvcrbq/vYDc4snZpzuJ/SDseulGK0vCZr32z8zuPpLNGv/LUI9/p/ObXymaGMvY8Oh6zTTsH35NVhsfA+HytJS3qIRcGjBrgI/cbWdcQ8vZ7QwSHrVar1XkRQIlw8M+FaPylRqitQqu97rZbrc4JTRzqH+ri/GsBiIDU3TK1aqxh+Rf/vEzVB/bRUmcMzfrO/cMyOkSZdd3Ef1QYjY1Lx37pCx8zBP0Cy1dnli1oYV0TrL72dgSIQ4SFGLJyisaIKhEDIConT5zgsNXunlfEd/2p/jYNwMJovzctkUKjQSMQV2pVCch6qgkk3gGF3f9ZLsy8p1Ms09ogCkpFKgEQMJ1KBVsoMwggJDioRf1FTc4xpD32QN51chsNdfpkYyyt0zaiI4Qt0H0UxKYudsXnue0O+C3EIhQnd7rjzCR7DSVqIel8mtuPJikppuG+3lev8PCxtKU6yzwNZdwhaOw564k8YX1hYdhqHQZLd3dOl8U4wWGrdZjWVoxagOUx50+jRPLxJE8lYhrAmR4iAMRW8TrtmJD8sELl3PkpVhoiVM/5owtchubGhY998oh3WClBRRw9oYXrIbxOtQBVcCzhMokRXw4KuZZ/PZVh1mRipMS81Gnyjqn+k8XhP1EjFSpJL2cZS7++yxBOleLc4ORODhTUyMeCSPpN1uX15DuZ4LBS+Su1nkyKdCoeTrl+kohe+WneFXIXDy3mUC4UuZaXFBmpLfbhFuRccsgdbRXYQixCoSF3oq5wi8MvdtUzQtHH2TCsoVT3xH29r8ppKGsMZewVZKdK5mv8kCXhgPCcPbk/rd/RJz19fG9qnfpTHo/HwSThdY4+6flCEwshtIaEawPJkchN0J/0eDyOVuCAzeU5ZUKnB5PtjMQxk1IL4knJO3ZuKcLhxv3oqteizE0aQhCKcujDkvdtjQ7QYRBBc6AS0GLCYjvTjgEDoPtAkwZNDWtBwqDvdNks0JJI4QhnFLYWAA2X/jMoOZ5uEBAE4oRWQHvkfkXYa8XUxUBjPq7c8hMmAPU/QmquoihyfB18vE2XCp9U1IKKyjwdzYbsFmFreZG5EGs37KI8Qd0GvQoYKbZ/y98pU0GJl/W9k6SQF3t8exvyVyYbt+ZmKtHldFu+n+J/qOWYhjLuEiguBqtVScK0uxORg+NjFw9oFMna8WETq0vMAUSq0UZDXLocjUUE8I6NA2hh7ZzVanXOmUC+wfi1YOs9YQi6B71cOdSKvBKg44Mt3tELEeZe9VVqxMgCwbqYru1TLSEu/au3fmIqoqG2hkO1GFbeWQPTx9qrG3wz9W3mECFAPPQr96BfBjDUEH7r3A2Q/stnjaCpxqRHZ8AAdYdRXADECAbxOp+blZFlqlvqqzSI712s1qLTEo2nLAUx/+VEouPHWg1UgLYyIUjFlE4gAPt0amM+CBr1E/HQPWrTtRcuRCBBCFFZDRBPCt+WMx6Hqiz4Q9mzFTdebALxpRI7Lm7w+WYOxa1Jvu2b6DfUP3IrLOwGtiz4b41cLHF76d3SX2/KtbQpN1P6Qtv5dYowYu0eivz7eOUpoTjKtbHLuIOgWho8LpsFyeGyWTh4wuJ1Wod+xn2/ZgkOWwfGqa9GZ2lfdlrt7tlY21M2CyeOd9ilGsDrtDq9oKVGcnk8rhPLsDBqd3qZvAqY28wWm+sPf9eG2261Dk7pMLZQXc2xlpvVwGLY+IGuOh3La2j0GLr6H60DDTUgnbFpxyZ8s3idf7ssgh5W/23UjyJbtTpaG0ZHfXidL2BkX5SldSIhQrD4bkbWghYMgMDK1PxqjHUdjbUIlTQkC1hV/Y7NQotJUY8mJkPEIaJ96IQSMRivO5BQEWTgZgQy0iIEqEp2iKmfCNffS+oVRiPqUjHWVkCACgSFhmFq0OrMURbyQluMeTr/iNLrc9flccbqpc/meCNWSy2auh3Jd+sDFLYpUXIVnQ0fv3TdaDfUmhLVpkJP8f+z9/7BbV3Xve/nCD8JQiIpWoQdgJbAVk7iMLajJmSlRu2cpH3q9b1B+DKUr6u+IJOOM49IPcPL1xe/mSB/4M4EmdZ59/FxqmtwxplkwjSqa7GpjPTlPd9eXzSVK5eMo9gKo7hSStAiaRmkKJIS+AO/eN4f5+D3AXgIghQdn++0MXjOPnvvswHttfZa37XWzhEVK1ksaoNGvaH8es0jVuE06Imjdfy6wNHppEmpXOnsHQrD7Og5Gk8MhQcYG/RcOjEkp3j43rfA+fjQ0NdmR/uf5SO9vWFgdvqQ2/vNoV7n2KDnW29jawecJz4BJ8rdInIqaxmzo/2+6Q+LE4favVPT7Z8/5mpwffWHM6yL/QPHuglNn/FFIDY8IvpDnzrnu9wYm5iADGY7v9MvF9B6lmeUlNVvP+TamJ157JnA238emJS773zy2LUXZj8bcL4UWHrIMTkRSwFGTKn7P+p6N9mMDfv1ZPeJQ6/8qpF/nWDfRz/Z3vj9lddIA/f5/fcHg2/Rmn7tqmIPWLpdzp8oCH2UYK2sQSH9cbEgRzVWhHXlKZU0DKaDnY23J1TP+6lN4jfLXbGptTxTwVQS31GCpbKoDIH1yA/L2pnDQ71lF9/fqNnFvtXI0iqJpbePGngJ25xPbUkpdnQRtg/dPaHjfY7uE2LknHyyHPtljMYHHRVbDoSfOqzy1GCVk+lDJ8TIyAjtLnAe/ziLxDpPdANj34kk6fKHw2E/weBEikOfDXndiKe7kkmZojjzVnT2hy8r/SZmcDdy9b8EZkzg6Olx0zH/wpUkizcBJudPPekGIEV6/7uXZ5iYYHzK1Nr8yoUoExNgIDHuu3TfI7EERuBWMDhBk4mFdZD9EQ2d7jY3jvuKJp9GlSbQtB9AEDA1YgGMRY4MA/db1pFA2shfNhkLiAupIo1BAFP5KUhtXMFuUd1Cc9eqaQyqUN+QkxoNDaXYU+75nSPQaWlfs+Xgnrvzy1HFJrRzkSB1iXfYIdaIrjToeL8jWzDC4wke8oe/+yfaAu4KnpryPtPrpKy0ZmxilohcBTs64vN4PL4Li2A90A2MXRrvaJGjN7tPiDPRFaX9Vb4omhkPejye4HzP481vviaLrwSf+tJzoVMP4DxA56ELF6JMxthngTcCUxKO3z1uxnXMRZOAlPr0p1vgaJctvrgQBWgxktrAcNARufWZE+Y8xSFXw0IC1iaic1Fit0respAmYDAownz5LiYBSSK1QgJIl+R3ejdhQBAQ5AzTJtxtHEwjASaV9AkmI+kSC2sFaS7FE2qXVbF/q26OAtjlr3NzaOTKVUHJtl7fXX6nyyuodq5F6u+ONqD9WL/pZy2Pa3+pTfWPHLRHVFafj6qRo+bfg5aMkDp06Ng6Lgd6A7OfDT//+fIrRVkss36IWKA3MPvZ0KnX8yTNHOxi6NxALNAbuGYino2VFPt63vj2hQ0J66e8tmt3HuXCazdZOR74/TcCF5z+vpXgXx1RnrqSwnTQvGY4zOr1JhOWVRYSODv5jc8+/a/fOPsONLfzsc+HGPK9ukKqcEdoeaxp8Y1yhqHDQiyBXK0qXWG7s5hYTyLc94h060rFRTLABoKkjQspVG8p+sOz3/Bc2/6G1no6/B35WyvMCPn666+rti7ZzbefLLKGrnYU5fPcUrIp7apVebQt3esAACAASURBVLBfDYtZZbidWM9NJ1wv78ZWe9jOs9WhuTS2Dh066gVn+xEuv+SR0xd0dsIxxgYDl5Nw3veqQ85zDXA50PsNHrNfHr8d8XmmPu3KEC+IB4wMX5A/GGcvcvLfX/k+sQxEAheAxeAwGKd9fzbP9STAvgwkrrs/93TLC2flSIcbExz47H37AImlG/zi0otzBWzFow7edga+0hSQk20Ljk+3xV6REyoYrCjJESTSAg0m1godA5aDJG5LEutyPMWtK1RJDJ2RuwHByBEn0berLdsmukUk6Kl2Wzt+46H69LNd7HJugErYZvGLTd+i5KBcg5Stfqre5TUsmf+9Gn3noLsndOjYXcyO9nsuJRwT144FwuFwSJyasAn/w3fphJzNOvRJW76p48HG5OXx2wKtp0NeXpnJYGhUUkfYm3F7/1jOzZCY4aTtnyazPgIT0OkPh59uy3D9uh3Z7L2UBKIjisYAgPVG8C9nsmyDuUiEAkRjQvJyYOiS8qeU1RiAzHqBO0JibcNYxEFI3C5/52qJoeXoBSmtqjEYGgpSOwjlXAfhoAN77qpBa8qHltxpyf3bZeEXtq9tqVqVnuKwEFpWoPqK1T2upL5On+q91ZeTsdWfVhUTzqYPal8i3dKgQ8fOQU40WQjrLMD8nVibMxbweICmB5HKshoCMPvyjxctdvatk/hvvreOORpuxJIrRIIBwNTiik4vfgCQiHPS9uoIAjYbqwlZRF8b9FxcEaDhgwcSP70jkQAKDAkWI+l0ZsOwQEo+6IMZW8IlMSNTHdJICEWGjUIUKgFCRkCNhaAt/aIafbHgycwauZTfBjMbpawG6XYsT7cUNrSMB7CYe4FovtRnFqtnBmfPDWwll3TNuRZKTqV7hPpXji0ZALRYJnZNu6otfkF7byUrU8dvsLCrmn9gWx1IyxLplgYdOraHEv5j7oqj04noDxcgcMxMkzM2McvMHXJ1KZdvsJRCjgt9+KHYqzeIBD0ej9Kn6RNPfzj7r1TayB/xU7EZIj96R7bYp2cvT2Hcx/7HQ50CKWDi/E/ETxskWPrpHVSiERJpMqTiue4kSLDKzJp87jfSilFQy7sklH2WilkQOdS+PUsAxrI5ZxLqfeZfQiM3YhOYP/dEPatPbAHlJ2zt578dosrnsCUDwC5bX+5JjqPC0euecqPkFXbujWroWVcadOioN2ITs1y9VnilULFwdDpxHZD1CTnTVMC9wtVrTM8TCQYmM7SeDodD3qngd97JdSAAJosV4MHO3NWHDgoYDLRYXnnXQDpD7LxvIntwj0e+fxsEKyZAwnTQhBbTqfx4GuOJhzdVBdS3SYGmXKKnslulqaUFEFRCLqnAr6w7Ok61lV0zH98hnaE8VqLwVg6qqsOmPWtvvDvY5exV91ZvUJ3GjqoRuzx6IXT3hA4dO4qxQY/MJXRcwwkr/83396vcTsGERyERBAOAY/Y7kSQPNHNziYXznq8+FD4jer77E6WT9CrjkRnA9GDXjZUP9Ykj5y+ztDx3RyKTYXGBwtRKdohbxY8R+dk60rpBPoqnbpe6AQwmMimAloOGRTm1U1efODH8j6tIRg7duXJXYBWa21mbJlGyBdkR4sVKQy60QSK+SKaA+mjIGQMkytwLIGjWD4Qms7RcsXylQAOsFUScVds2sx6QyZfn8g8oTEvbjugM9bWT69CIKo6VGhiXWtw0O/fNavnZbHN0LY/rlgYdOuqK2dH+wFTun961weCUNxQOeV2snP/BEsSXKRHfZjsHjcxdGre4XDezWY8mgp5ghDsZ0lfPXk6ynqT5o66mBjZujLc8erwVMJApynHYnDsBxIH1yM+UexnVlEeSlL0hsJhLBjk+HFlV6llMTJBACaxIFDjchewYuVdUrhS4BjIUsR4ygEHVmpB9sOyeQbV1FY0BkFjXftAqv5v3bcx9/ntVn90qdtRrcE+w066Q+kLV5lGzbWZ3LCi7o1PWFpapKw06dGwfMgshVxDrdgo5X/WJWxH3yeNOwMSRziu04G4CAVsTYp+3DewtOG0gYBRITOY4f9i7etx0/db9HH8q4BAAln4+s9+AwaqUvFi4DSAIOE737QdYyslpwYDVjLmwpHXuf8tReaPIqEWuFV+zN7nLO7DRlK/noAybKeA9lM+kuAujtXRoGS09n1ErnFmpm1KYbWpXDQfKPCm2P/+8WsvasNWcPLVhNzkE23GFaNE2atZIfj3CWHKvv5vOnS1BT+6kQ4dmFCVlyl15dZXDT+Uuzo72+/7+BiqlHHIQsB/gE0+HGPJdjBeHI5ohhe0TPY7xXzWytGqbmVwFEATa/iev6eUfLLfEpceefuifz15OQsPj3vt/NP1xMTIaYR+2DRISGQOtJu4kFHbi/gdZu0EaDIK6JC5A07H7l3/2bqnTwX7AHK96wq8Mi43EarYfuV9DSfZIwW4inpZKB61T4csa0eUPK1GXWpI7VYeq8CvcdDc1nqvevYeoVzKrTXMgbqnzrWL3B6ohZrIkpqbuk6wtRFO3NOjQUXfc3qD1dDgc9ndCSyuiPyTasYsBrxuHFSTiy0SCvkhWYzgoV3c04XryaResjsslI2YmExztxCIgSdz65cUZ4ncXiUfOXpUQgLUfjcwy88uIFciwKqdtzrCwno9nuHtDGaJEYzCYy10Ay5ffLRPWErVqDCBrDHLq6Gy/+0p4VFI8VR7ysIvy0dDZWbZx2r64pTwNW0LJ8bHKqf3X49y8N1GDxlCb/aMuBNWdc4hU6rP6VHUipA4ddcLsaH82A7Qg/8tyHWtjYp5I0AcQ+csJF8ipk41IaeyQMZKBZCOGJQSJmZGzue6skNlgckaR95kbUbKFHNcVnsIjXY1X3vgVycr7idVMIqkihTMaNYFNMjerw/RAU+rmsmJiTVGYPWHLNaVqmkzbYebfLmtZauUAMB1wp5govpypJxGythSHtbXfHWznjfYCyiViJYKhquz8NSOx5t5Fu06jWxp06KgLxgZ9F0+G5CjKBlieBZhexZhVzM3WhcszxNbJZM/ScVhLk0wTXyIJ6X204v70byjJitYhJZEpLiFdLHSvjC+SLEsMVfivfz2nMZSkUxSUnEib7BU1ZT5I3VyG/H5Ury220GthqHrcmSvXGFDRGID1Sz8sywiVKM66UWVC2k6f2k+K7wmC4ZZOz4VvVJ4uuvxl75V9RVWT2H3saMRmvX5auqVBh44tIRLMhkpmoZSK6A7L9aeAAxZwArQfon2FI18KMeSLGBH/LNR+zvdClGQawG4haaIxQaqR9SXS1q6F5vlX/q1IuBnMWauAocGeaf1cyH/nq76XFouF4v5G7iplrAQDkiqdokT8S4oMrbo1WR+wrN/UXlqyAjKaGBWbo3CqGbXE1BbKQjo3g+URR+JKbPN25ahLCGWlHIJ1PM5uahWo3mA7RgXV/ImVbuXwa3aO3+r6bz8utPwRLb9V7QYkXWnQoWNLyFWTAhRqZD4XQ0GzXNGjRNY9Qf6DINDWGzr1uu/iCksJGv/gac6fjd8Z547ykOAIvPTUjzzB8Q3bI7RcIQaZtRXLzMs+X8yAAIKBfRkygBVsK9xVujVkox0FgQYTqzUQEppyJbHXb29bY5BRpDEIGAquqLAerb9/NP3fr1erV6GOGiarpjHYBmrgNGxHb8j1UHdsKi2qN9ih3BL31pRS32TPWoargioJvsrvbpVKWW7gqQ495FKHjm1jbDAXSumpaLS+GgziD/tFEP0hrwszrmyawel5oglEfzgc+v1sYaT7Wwx5Mbl0m8UUsfNn48V9SrGAJzgOSEtXiGEUQEBKEAMySCBlyMgBiOuQFXySRDqT/7yazPsqSrbJattmQSXsbekMlYM88zqEYLWUN1vfisZg2LzJLmHXRNG99WL8GlgFyh1G5S9yb1+tjl9u3X8nutKgQ0cFjA16gmSLR4S8U8H+l++UNbqzRKzTP9A9Oz2FeKLb2ev3Ol1tZqamL4/2B98G1rl6DZyffEARb+8K+7DsQy6QvZiV8TYBGr19YmHf9zcJ0HgYOaeyhAGMTRgFBLmrBICwv7naS0gl/83+WfJ3o9qzFXab/aZNJLXFiiE/pFBt15LW17WbXVVHrRLaWozW013lE3Gfai27llDXDktEdd2FikZhrJ1SoOqnr4sI0cjS0K5e1F2w1Z1usnNz2IUh6qvn6XkadOhQRVlOhtnR/uBEaqbpC6H2c89OHyESiYr+wPI3A/92P8s3QM7fDOAWRSJXo7IBoMTRLvqffDP4Qnw/ybulIxqbScu0R7VEBVYT6xu4v9R3a3hYftQuEJcAWq3cXi94Ivu4YGDf4T9unvz+QoV3NDeQWVMRuw0Ca9JW8iUUtzTARsmjBXkaBMiAJF8RMEhCpmQY82/vT/5L2fJsDrNQLZBEM6yh8ItKAEVhnoaf/jSfp6GST6Hu5LXybjcN5dcipVSb1Z3TUAUaR69L57WxAbTkMKiyOBrnoF2l2HSIXDPVtdX4PWppplsadOhQw+xrF6PiiULntrN36LnAc7IWEY1wJhwOD3S/u5RUNAboPILbGwqHw0MDJ45g6+lxI/r7nODuC/tF3G0yH+L3HrWDhOgP+7swK527HCbSlr5wOHDMhFD4D/ZBf6jHbpCpfxmiWY0BFI0BWFgvJTkq/82Qmfx+zvFRvpkk1TQGcsWitG/jxS0zlR/NQFpQSlTID26Ut03VojFAXTQGbVDdW3dOrFYRLSUHTY1CqFykVZr8Dp3FqwjRe+V5qb6M5etQlxwMWrCl9RfKUkmWqBHVf0tamulKgw4dFeBud1W8J57ohrFBz/CKFcdp2X+xNGGIjvg8Ho/nS38dEc+cOgDw+B+JsHp5eir3qPOJz7kaTcDs9DzONjociH7/cTv2hx8FljaQBBynw36RlgPYbwR9F+IZCupPm8hlaRYEMKnQmR2nfzfXJmfnkCR1BkD59iDZMAACZiPlnAPrfluuG9ejjSq7i+OhXANDk9xjYe/FY5U/fo+Nn1tkYO6oiVt1uF8PlOtbuyaG9w5KFkH7D+keel50pUGHjgqITiu1IGZH+xUyZP/obO727Oi5iNh3vIHYL8cAZ6+/54C5pSfkF0kuMv+SbyRKJOj5B9zgaG8kKhMixr7uG5lZXCQS/M7leaJzTMaIXHpzOQXERvv/8tYGSMTOe4I86V6X/R15uW4zQTqfrUGClgOqmoDdCIYGELAKkCsBlTUs5ItDWFRkdDqp9J5MZ+tbFuxR63dXc/aJmTdXVLal2LWM/IgR+zJNakUfzDsgFLbMiVRnSaRf1JinYWtj1e8MvVVhsCnp756gREXYa1rCNldM++M1SPd7u1a60qBDhxqcx0+6I5dk6eHsHQqHw2F/EUtR9l88/pHHcEwE+0dnwfmRx5LNB968FMFsZmJOdk/4VyPRpTt0f/YoCRaA7sMODjzsorNzfOUTj2Cmyx/yTg3/JM6hDzpg4U5O+EV+sPiYG3cbnQ9IWam9mso7LwRAYnFBJcYhdv5H67CxjsHCQ20CZWmkNzp+Vyn+VPBwXoSmUaQ+QANs5fQvIBiwmUGCNMuwvFp4U+m2uivBbKgYe1GCQrGvmROZw3097vKB7Oohl9uRIjWcoWsbrsrJtfDPuusQOZVoL4j/uiwdm4nzTUep+axfvefqdJBKM6neYEvNdKVBhw5VOHvPiJFggWlh7FKkpIm73UX3CTHm7nn0os/j8QQjREeG8YefOlRm9O/+3zqNXAl6PJ7zK+Jf/Plz4W98Izw04BPNjAd9I1ESnaGhxwFaJXMGRH+fm6Rw7Bkvc53cbMUscdQBRszNPaGwvwvZWiCAlY79xWMJFhy0GJEkMutciWX//Vt4xKFMKzP5T/JZv7MTe3aqitDdfxRHoTBey34wySLfbsraCQyI/idL7QgSUsbWcVh9VRU2g73C7SySpdzIisgrCtb9VZpRaFzJzyZ2IVo+0L7Z8pZKew1ioO4WBS1CS/tTW22mBaphGtvENidW29tt9alKfNjto9JMqtAzq8+8jr8KPbmTDh0V0D0QDo32+zwjyt+iPzzUjZzzEVD8F90D/kv51E5ub2ige3b0HJ2HLlyYgGCwS3Q3HwCcvR81T1xOdna6jz2RLW4w9p1IlqZ4cMLn8QDYxaHwgBNmR9sYGfZNyt32jX7ncWD222d8Fy74PBcAuA/xf+2bCg7PGnmwiRu5/ApSghi3s3/ZOlpS0cWNfTQ0QKNRMKTMAok0dyWAiQlaTNj2sZozOdy9zl1MQmlGJvalpAxIxFMAggEJYyT4gsrKGVcnfoVRIG1+2JWwN9vHfxEvls3xzGETb2+9EEW1eA6z++D6lduV7hZm4C4oQtHh4O1YiYli5eVZ/qS2+hM7lA2pEspDJ3YUWxpo09fXGKGwl7HLX7cqdn9Q3dKgQ0dlKI4JGVmrtbN3KDzQnfdfdA8oDbL+C2f7EQ59Vrn4B0Tlq8e+9KRbFA+tnDzuBDlv1KU/UHJAuHnYr3z4xImsvGpE9Ie8bjo7OdKKHAR6QY7p9Aa8bpz7gEdPunE2FmgMVsRsV+42HFb2Hf7jj5qwmIivYz3saD5AsxE6+7osGB9+2uvG+UE6HGAHO50PQosVWJfASEdz9oQuFVn/La02KVMhmTMY0mCx7gMhfXWG8YmcxpB3OswoGoORR1xboCKUb5CGjv1Kn3eqaQxFKHiRyVKNAci8/qb2GWVR3bqwo5SCHWViqjodamDjqyI380rrs0f8HXsWtRm9trmqutKgQ0dtqOK/6D4hRs7JN8YuRdyKmuA8fnIqMqX8oSSDyvU2Na1qFHceP+me4MFI0OPxKCU0mzuUe43ZvJOHnwr7RdxiF+5Hm9Z5NfyjQc+zPBM62cj6OsllHmxkVcK0zlWe+kwzqQyODzOfwOq4D1g51DWx7iKOw8bUDQ7uW+9wYBYgzeRS/oRuOep2iw/JYj+xsCoADdaiyWZ3IQOwvp6U8lUwBCsCSi5LAGuD0jbNlZmMQCE3ocFlK+jsQM+TbVRBZvKuJmm56Q4ptGBXPn6gPOETVbdajakRdjPIoi7Y5XTL9yqmsRz3RFmp/gOT/0+79qlFyavtBXX3hA4dtaKS/2JsMBgB5Bud/vCAYjtwHj/pnm6X/3D2PuPtVzwSblF0R6dnQCXE09n7ccfI+WUH6zFA7Dn26oXLKfsnAQ59WIyce7kHVi49FYw4vKEvHvmKLwL8fDgC+HxA69EWLp+9AAi4OuEXgZE5AM4PA0RCb7nN0ci4PFZMvrVgbupqTMYWi+YhkLgejV7PX5AMsFZcYTO7iZXXu5BKSnGur5XGYObP+2szqwWd3bnwQnkazhqwqbCWFsm6ilTTY+ah0RD93tIPdgiyWKoXc2J3llS700GqX5XwSrJcKsuycM9/V3pGSB066gol+bTsy5gd7fddPBka6nUqf73YPrS1ckizo/2+6VPeqeERxcuBvcn9uT9Xevzelzzn5cITD/rDZ7vhcqA3MPvZ8POfB2YHz/giccDRYY9NHvGHv+Ea7ffl+imErcOxOhmzN3PIzJEvhQe6/+Z/8Xz/rrVU2BsbHGdOO0ZGrgAYm71/+Z9e/3LgqoShhcyiSrfKhD9A8h1jirRkPmxPvl1YZUMQaGwiuURSe/bJHYcrHH5O+bhpRkgZO5oX8h5CI+egRHBWylRYl9G3v7abivl78m1uJzVkeSflfJFKnM1avhddadCho34ol/EALm/Iz7O+kaUWFmXhakfsDw90z472P8vJIyMjEcgW0CwqmdklusdXjrjHI0p3djF0TjFczA4+4YsUCnV3Tw8Xrqxy+KnwE9P9z07vj/7zlUd+x323/dSRkeFIi5HFpmOtC5eNPT22C8unvFPD02fCA67RL/tGZopfwu0N/fuXfGeXzUdJXgcw0PGl0OG/8kWKy2q5+wLC84HJzSId7Z2PxH95pZA7YGsivaxikNgbMHlDfyvrZFqVBnaRjbh9bJXPqL2x6oO1Pa7aSaV+tE9Sy6zec0rDpvmkq+u4VXpWhc5p0KGjfpCTNzwKMhsxi+d6nc7eMyLNnwkpzMdDuUeiIxfbQzKNUuZBZHmVIa/b7X28HfeHPngEd0+PG7HPeygiZx6aHX2qWGMw0XXmsRuzzGWDIFamrohfCfvagUefEO2sWBFPNyWwP/zYAaC194wYuTSGs/epY2YcnfcBBpsVMHxoqNe5mAJZY3AAGSaHfVfixYZSiA5vrjEA8YkrJWzDzArmTXwAWDbvuBibbrsqhEurqJKnITU9U95ys7HeK5SFrbq0dznKtHz0TVF36oMW3kDdX7nm9AmV5lBFkdJoYqkEndOgQ0ddUSn59Oz0lPsIz3o8UdEfYp6r16AboLNxRGY/dDhgBpyQtzYEBAe2V6Puk4EDFy9c+buRKAQ9kZacxSKHFNf/33OLSUgS+a9PRZbmLHamnvf8XwLRmM+HGWuSyHAEiARGgKAnAkQ8ik1j4haQue83TDd+sQGMRXIJmbJlt1nDIkkJIeuzkLLVp9QjKISOw42TN+LqPodEmkT2MfPHfnvjZ/9S3smWq3JvKmBU9Jv1V29tpf+95l3eCaiauLWYxNmZNakjb2BLg1bBDr2yKglUvljuXNA+aN2/FN3SoENHXVEl+XQ0Mt8IRIIFdv5VJua8oXA4HOpZjDEPwNhg8G2H2xsKh0PeZMw+MU90JDASZTIG0Gxnxf3Hx8xYzdibEf3hsL8LO4uL7Q+bsRhhSejpcTdAdI7rMXB4exwp1qGrT1TL6EwutuDGL1KQecvjCb5T3ubgMYuhmNK4cf9jFcMlpcm3cxpD8yfL8y4ZrEp2yKSaxrBr2Dj0mffeHrhzJ/v60uzrhTpacbYa+3qvYj5VX3mPfCPvvX8wOnTsXcjJG94EIsE85VA+tU/PA3L2hsCxbHXL6RXsHznuBJynHrMzfw1g4W1mnF/odYKz97lwf2cMuxjwumWXh/9jcZydR5vMGE3El4gEPZ7gOHEcp07KEZD25tg7/za/hAMDzUdbzLHXLsckBAPzS+12sHLAhLuvzwHYekLhwDEzRjALmGyYgPsCoh0DpXbIu7+8U3pWX76mZg8wyM/lt7WlV5WqlUJDUy775Hq+1mX97Z2dH1fRZixlOSEFPmz7YZkBwrU1quo2UV0s1ZwjoRJ2NGPEe2KgQnm86eKrft5TKHl9jdqVahuNX5PuntCho45w9p4RPcEftiikRtnRMAUQW07i+HBeHK3cUhIzxCO+rJMA+61ZZl97Wep5fDGghGN6Q0P+Ls83AyNJxafg7gsPtX7dU8xJxMLDreQyPC3Oxc0PffY3r3z/6vVFWFxuguUM0Rfk4NA7cGd42G7BblaySwpG9qXZJ5sckj+el7MmFloAjA+k1m6WvKy0tooKlOeKtiA5OEJaW1Z7QM3ScNDK7XWV69ow8bqKLyJRloFSYmKivJ1Kvuls+3qbyqsbunfODC73XJc+q6xJfQeqPocaUPPy3hOPSRWoshY2nWHNb6FbGnToqCu6B8IB9wqRoOyaCOIPD/U6wdHZxsp5+eK3Fs3E5xU3hlxZO5cOcuzFkWj0woWslSI68tVvu77m/x3lT7c3dOZfPZ5Lx7xuXEoyIrcounKSzmSnzQKQvPz9q1lpbDlIl+jAQEsrLjtmO6I/8JBEfElpIKXZAMAoYMpEVj5xJEkxczCdjXXI7bQCgFnLscNQQzjl7S1zGmqCobHcwxL90vcqP1BymLtX5uvtoMoButKpvbpQqXTAfU+c1LeK3Net6j6o44+hvLct2VS0WB0qxWFWh6406NBRbzg6nfnoibyt28QnlIv+k4cQP9sNzoF+Mfa6THmYmY662110D/hF3N5QLjN18wHoHgjJ7omhXqankAM0zI10OHB7nxkY8HsPKeOaVpiMcT1WNKG5KOORGBnurrIUFwxG4N3ZJPZTeV+JBKlVViVSK0QjU1DCHFwwydLV4sJOq0nRAzIZjIKiXiguF3OHnWKUnfpL9ygBjKXBDVtWM4SDmykw1vKtMbPyRunsBPfzn9c44r0Qijtt9i8UNvUNCXnPKVjlS13lG6/vj6FKrqdtfiOF30Jtc9bdEzp03Et0D/gveeToCbc3NOAsvgSiP9xbUjwpRSToy/85km1aeBE7ru6H516JfdCx8PMYuDsd0YkY6TXiBunon4YHumdHmxh52eN5GcAE+1tpMhCbJ2UTn/nr3/vr/zkfTmmEtPX3fmPjx29myKzPAAvyDQEDbDRAQgBJsUVszMVBMCBtVJT8pZueBFINha1LOrm9GaFyXdNeK0W/OsY3dofWUN1EXOnudrj0WrDTtvc9Enii0Yafw3tI3amEumg2enInHTreO5itlNLR7Q0N9TLa77u4Iqd0zEdtZm89O30kFYnM2DsfafzVFT5wIDaZz88sUw7sJqWCpegP3foL389zUZECSdNBkloLQpV0+56Dyxt6TtbUCpM7vf766yXtNs0CtKPSdzs5iKpMrA4ZAyvnJdxOt/cWlRIoVWqwzRfcoQRT1RUFPbmTDh2/XlDRGORyl7LbYrT/2ekjrugcV68Bs//wz0qb1cvP9o/GpuejkcgMEJ+4ElsnNmk65qKzU2kj7xbxFGYD7i5XJJjXGIB9JnIagwAtrRw0YhCUPwthKNnryt6h8qbV1qx2dWcPd8KpsL+p7OLHniu17VSA9qw7qpv1PTTX12Dl1jjb7Z9ld2dZtj9KOeuz0q3td75DC1LbnHVLgw4d7xHMjvb7XpWLOUmyOaGi4aEiWsWjC5F82amW33Ys/UusdAtoecCyeOvobzHxUzneQBCQ2h9puXGlKKFUoRkh+9mQJTAYBDKyF9iQr3VZZ+yQHUNwh18aUj5XtzRs0k/Vw+L2j+AaD6NbtXaodqv94KtFtml/fIeMN1s9x9fdXqL9Lepuctj+AuqWBh063nOIzlFYLLsEZnAcxWUFgdbT4cAxEyBA5vh0dgAAIABJREFUkxW78YqiMViwgnXxX9UKSy/eTJDKagzI28uNaEkKSkHWEuSojWwnOfUgk72yqcZQMT3UppDFWfl1IUvJ3ByH/GGxtAcpOjhW03zqckTW/riWY2IN5/4dJVpuM+Jgr4Vj1GxK2WYP28H2qZQ6EVKHjqpQdQpk61buzHDTZ8JV8gtNxtSumnAdxCQxP0fGyFLUJKRTwMJ5TwAACVZSpGMKhZEE6wB5TUAwsC9ThY14t+RvedfJlOU+MDTTYeB6dpyDRxGiLFTmKG7TDKGy+UnaS2HN/7knUtZDay3JncqD/nchmn8Pdlv+1hr5E/ViR9Z9zXdHru+pxA/VoSsNOnRsimympk2hTjs4eTGgpnagqh/cWSLy0thAt6vwrlI8s1RjcB1zzSyl8hu1BCYTSYl9+1Lr8j5M9kRuoSmTjXrIwtD5e8aJHydAsHBIYi4DYPjQkwfeekG10rXNxGpWS6jkHMgscT17W4C79qdt6bO5uztHjWxtY2luyypIxv0hIfpW8ZRqt31sAVVIgnskuKAQUlndjdx11cZV/tQ+0HY4mFRdxr2WnanKtPfaVNGVBh06akVRWWu3NzTU/qIcr+Dwhp7vdeYbBEaUi9kGbu8zvU6gdyg8NujxBKGCXjL/knK3S3Txk+GR4iyQdvGpzqlASV3rBiPJ7LG+aPNJKBqDAXKxkJmJH29k785lW2YqaAyQ1xgokf3CI659V2YKJbYc5y+wcfXscunlQhgNpOvCeViY27yNCko1BmBt23MBNAiwXQjn22mRsxP6TZUOVXWX2iZQ87S3v6Rb7WGP6Ao56EqDDh0aoGJCcB1zzVx+64gb8Uyo/ZzP57G3eQPei4FXV0deHOuVNYDoyPSZcKi93/f3MyM+j+gPh9r7g6+/m02tYIUmbyh8/LUvf/UHQTltdEEW4xUmsgrBeORdbHQ4WLzFYlbGxuV6lcVYS0MBIRFarYaFRAZDNltziYTe7oZkhDRIeY1B4OhHuD6R7T2TQDj4kNT0rhC9I2VbFIy6BY0hR66sHw7AnbKLdwfH2LKHYg+eCGXUwKzcalTFbgpg1SRLdVzwcu2tkpWlZOj3tB6wJehESB06NkVh9Smy6Rr9nSa394sDQ+GBbufxk26395vf6nUAtkO88ZwnS1OMBD2+kSjxfdjFJ7oBZq5moKMvHO5rW8dy+LgTSC7GDTziD4fDp+wpSCyM9vtG5iB/sk/LtaJTGxWmKOBqc3wgewjI5CiKLKxnkHL1HYwVSYIlFRcMqkff8otlZAUppzFkkzxKt6+R0xjQpqeojb5NjcHQ8UhZry0Bb2n6ShBQL26+GbZDMdMYW7Fz2A47bzsM0Pomndwmi7NSZOymM9zm6hVi7+sTuqVBh45NIfpD7edU+YkxmXnwBKQmgv08dZKSGkgub8jPs76RKMmCwlQkgfvtBubGfVkLA3enZ+k+ahNejs+/MiJXhbjPH/52N8wO/UffK2mAeMmWYqDFyFICzMzMFfEdCimK+TN6mlQFWkEJo1FdQlfa0FT7FKwZ1ms8B25n5xQ+aZdejZddzrx9pezaO4GRMiYnLQN1IbnWYIUuP8XeW+tF4ehVSBiF7e+5zLvnE3iPQvsvTbc06NBRXzzYJFeECod6Wsx8oN0J4GoAe08oHBLlk+3M8OAYbICtp8ft+IABIDri8wxOGoHVlh43dgHDrXOjs4wN+l5Zg1Rp3ITNiLDBYgIJpIL6TkLZUSAjYc9aGAr3BE3lprSgcJ/J8QhT69yTPVxNYwDVOI3OcLjc/FC/tBLbPEaX6BC7nAxKlT1QX8OAxmmUvPXeOZrvEE1kj9uZdKVBh44SzI7294/Ojg16SuL1s4UrZfS/XO4Nz2JDljsHmp3eL8q2iXgSofGxma/6IgkOO8DVNRX8wQqwEYfYO2CCjj6/GBm7Axw4dgAaTWQM0bf+x2Bwqut+wIIDV4cJwNFGR2trIs0+E/ZmzIAAAq4+fydIaWxGLEYEQFCsifGkIs6NBXtCsnq1Bu17V0FLw6aJGczClvquG4TyQX/WP/puWbvN6mvew0yOuQnUHVs17O+OqC4XZpumzNrOV1M9GKRkiSqt2FZXRrX9Xk6IqbsndOgogbN3aAggnPNFFBWIQgl1mB3tf1Pt8QPtzL9FJCj7IrKURwPGNgf30TLLfgsYj50Rh4f+Edb/u1wFOwXJVVO7e+FiljwRSwKMn48A7wIJYsw0AAKxOWDBBEg0WsgAEhj4QOvCPACrOW1AKmAdbACkte9pUoHfYbNYyTYTcynIn9PVHjB8uiXzymJSYmfM2JvMUVK5Gx0pD7pIXYZjFcfYMQoeZR6NXdZLapZ2e8SHUnJx0ywRqqjebIcyVJazO3dilPLeqOk3rFsadLyvMTva7+nv7/f0j84qfxXaF8YGPZ4X24fC4bBSmbqw3LWzd2io1yE3dPYO+TsVKuHEhBLCoMDVCqxnSP3cF4ywuMjEDZgcDkZkgkJXhx2LnaMOZkYCI1EMAtgAOhy4bHIXNgxgpUt0GfblBWMKUinSYDViBDKMB4eLIzwwWgr+2B63wOV92lFpW5OYS+XP8Q0NRU/mqZeZVxa1zMRWox2iTiIr85ffq09HtUAqrkx9r7AlF8Du+yxyqK5X7XTuxd3J7bgLo2zJUqIrDTrez5h97WJUPHkS8UyvE3D2PuN1R86Nzso3R89FRP+Wgu8cvUP+LszYcLeJ/rBfxO31f93rxi124fb2ibjbcABdfaGwXwQcB6xJEnGuZ/kKCQvi5x8FDj8V+tRBjnYi+l8Inw30PMB4ZCZebDtvalCMhQYjmGkQsDYj+gPHsoI6XWJrL9lzjMpFqxmztfiWwS67PJQnJGZGzpZVqShCLqJS5hY2931GnoXm/IxZrO5WbUyBlp5yToOx7/Nb7WfHHBaqNIItRVHWMLG9QxooxzbjI/YyNL7aTv/SNoVesErH+x6z3/6C70JRPqNOf/gb3WODnnPt2XzRqqWhOv1+WzA4TpvX/4cXvzuRMnX6h3qdjH3dExyXWygpm8YGPUH6vFPDI1FoeES8/woPuSMvF/ZnF0PnBhjt9718k9hv9nmXhn8wQ57QJ/rDAwx6vrt8cObyIrYmuv+07+3g8GIrj/UFlr8ZuGYkvr/n02sXXlGYFm6x82Zkgo92rf/8J2DClryv2XRrpYnlW2ULYMTdxPQCaUMhC7DpPuPyrQwGiYx8/ai/LxMcnsw/lfN8CCCZf6sj9dNJCYMFQ4JkcQMF1ie91r8dWUpRlElCKwxGIZOWtpNUUtujTeFw1tSgWrCqUprkwot7ATVPbO+80aYzqSQ+qxj8NclFbe6A3Vmo6u+4zT5rCUzVlQYd72OMDco5GilMyfi9L3nON572Sq/zTOUSE7OzszMv+i6dCA90y6kfP35o5PUPhYZ6nQV9FsCB+5T/5MVz04eikfGC6+7jbVOvLVj43T9TqlZOnwm1f/f/OD9zpzwxYdODLN8oumK3INseTEZSJRJa9Pvng8Fs/KddDHzueuB7M1mKghFJwpSRrQItdhbjYLDy4RYmblZarS6xbTxSyAEwN9+fWXo3AwI0NbO0hKwNmDlq4/pSQctGWIEquoLJTEoxSZS2sWBMFKgfAqZGGtdY2qHimbjD4a1Uudw7IrYEtU1MNbtRzUNvczW0vEL1sWrQG7YkpHeHz1Gz9rMT0N0TOt6/GBsMTnlD4ZDXhZ3IpSyX4fOBnpbJ8yOHzvQ6Z0f7+wcH+z1ZpsPYoBI7MTjDa+emvE90A2PfGYlGR86PI9iccp8Br9vdc0xJG+T2hsJ+MUZ0JDgyf+SJr4XD4T53bg7R1+YkMopIdPYOhQe6nWupOwYxVECkCIl27M0c+3yfGw62Khd7Wogf9YfD4bD/aKnGAET+67cKMkbEI//n383kz9lSGjK53AyLskkjs86EXXSV5nkCoBUYj8xhO1hwV9YYAAlZYzDbMQJJri9hMgAI0NHn71xRojYyAM2PNCm3yGWRSuWdGKXKQKLYYCGRim+iMezfVvkIy+ZN9hRUgxJrs2DXV2NQ7XBL0GKxr25XryPZopLk3mZUrZYv615RRlShKw063rcYuxRxnzzuBEwc6Sy44Tz1mB17o5wXMBq5GiUbb5kzIVwNPsupIyMvjjE2GHznmMvtDYX9h4f7B/8h4j553AHRC03/TkT0h7yM+DyXToRDXjduW+TFMeDRk3laZainBZNA5KUc/3Ls7ZjZ1ATAilICOxInvs78S8NRuL2gXLywCBNBj8fjOXudlh5Z+zECnf5wOORtXYpZXS4zXX1eN+LprrhSp9J4LJDVQ7p6HAiwH7c34HXTJbpabkWO9v2RIzuVZjsGmfeQLXW1+ugz4XD4hEyAyPCQ6G6T2ZqO0+FwoDNBQsDWTOcDpDM0neiSbMwNByfkqA0LNgOwdGUZsp4CDXkem/cDIIBdPVFlKe6qqhRGFX6lYC5XkTYLuSzBdrzs2ydDbBqUWNvEtvNUCbYfBrmnROam0P6+W9Wu9gifQ1cadOgoR1OBJBGfUmIm/GI+gOKprugLwyv+E5c8wQipzi+cvOgb5IQY/cW88pTb+8RDAHKGaTmBNI1tTE3PFg40Nui7sILjIO65LP8S3iW5eMHn8fhG7sgjBo6ZIM3EBA4rVjN2O7IZw9IVCofDJxJgvl/WfppMitR76y6GD3yyDebpPSNG3m50u71PdxggfTmQ1UPG/2kFiZZPB05e/Nar84xHZhY3mPrBxYezeZSX4jkrSBaRoMfjubQOIJi5FonOrWI2wt074EgCEqtLTNzEAKtrX+xKFTAzEqxWsRBUNA8syeqOBPFyFUOzUUEQVAgNUrI0FWZZbkwN/Sp9SUUXN5Ucu8O91y5xdyE3wL3ClsRtXbgC7Mz77gUVSlcadLxv0X1CjF58+dv9vpEoExPZ3E2DY8ByCjlF9KuroKR7ujw9xcqlfjk40/Uht9P7xe7uJ7xuxKd6u3uHwgPdpmXz3OrR6MXXYsCdl89FxBNVQy9mR78c/AkkmYkRnYuO+DyewTHGvp3nLawTCXo8ZwL/tgES7jYaG8kkicdp6wscM8O47+tjs795AG7/zbdnIcVKCuaCHo9vPElm8oXLSaLDnmCEKz+JRkfO0owV7LnaCrfjwOKFwN9Pz0zKsn2Z+WQ0EiudaufRfOSkmUdEN2aQsi6FZBqS7/zoq76JAoGbho2Z/zxeJoKP9h0WAMNDjSU3MgCChWbtzgVBYENFbRDUCmyYte60M0+Pbd4oP4Hyz7ujDex05zVLpr1pFdiSuL0nuS+3g91MOKYnd9Lx/kX3gP+S4nFwnA4//3mQWQuDn2yJ52tITgY9EdEfHjg2Ns1bpJZ+6BtsD5+YjtIOONuPMJ3t793FJI2f9h8eDowAUdEfmu73BKPkUjzRjDufNmj2tYszbhtCI8B9D7vn2585M+3z/Bcw4Phc+PlPjXp9I0tAnDs53r+RAyYWUkwOBwBM9nem+YOPYI0sXvD5gOZmw/rqb4XCXX7P2dsF77qeuK8Tq7lhRtpPNFs8UwBJAInbBeaE+BLsd5GeYR+GjFJHe+I6mHBZmKPVGb+9nEIiH41gQMiYp/7qOggYDaTTACaB1Py7zVaW1iloy/XhtwEy11ayI1qsJNYxCqQlpASFBMoiGGjYYK1wG5ckCjgQHQ4l07akFuWZUEt/KRiRSq//1tmtlri8J6jEP5DudcWKkjm8R1E9Z1RdUK9vakcTjqkMp0dP6HhfY1Y2Jxx+KleManbwjC+ygTgQaj+n3PrgX3uG6Qv/0b96nnvD9ZlvPNc783XPN8dxekNDvTODnqEIcaDNwVKs6yvhr3XPjvYHXo3GJgFaP+o+8LFnhnoZ7X/2H29Gb6wj+kMM+a5+wmuLjETBYidRWCxBoKWJ1BKH2jh0pOWN8UVZAro6Mc2xOKfIVFMDqfL4ihxsVtPaekr+t20+7Eq+/a6BdMYK66Utm2jJsBQvMt2bOp92/OLsTNElOg4wuVDysOWxh6WrS4bMO2sZo5l0mawWcLWRXGY5RSIn28siKMxmBAOJKq+zQxCwNGYX34zjsf1z43clzN7QqBI1s2n0hCq9X3vYwrYi37YXtbGjgaN7iu1fA6poPJu+xU5rbOX9b5pau77z0ZUGHe9vKEpDUS0oBy1JHvtyXml4YvrLX/0hn/nGc7ZnPUrOxTZv6Fu9M8XRlQ6I2dvETzRGIlHsPaFzp14844vEAZco8sYbM4tmWpIOd2PssnzWz2YycJzus53PJnM0IezDaWKmuOxSNt6yySQ7T2SYS3InGVvt6YXsg43NrCwBGAXSCEhSh4O5OeImMfDk1cBITACpCZfTOnt1XQJwOUwzsco+/SYDdwTlaG4SSGX3jpwaIAj5bM0NBtY0REXKe9rubUOb52pwhsMh5eOWQi5LOA3lFzU+qxHbEfPVzQDb/zL2bBiqRmxHaSjpob7vvlUldSe+CJ3ToEPH4SzVUYlnOOUqvDXQDZjMLTMXX5t99KSrxQwmmBvx5YIpjopyMETA64K5CGf8ImL/nzjHvhNJcrQTt/c/EJkBxKfCv2+OTbQ9LdrhwaPsw+XAZWPu/HDU3RfyttmBFFKiVGMAlm/sP2rHLv6OHBF41B/ytkHS2NqM2YYBsNBmTRv2g9VmAoOhQGOQQJI45P+/n38aCcFy/bucVrJGLjOjaAxANY3B2MJyJm/MTxXsP7Ju0CAgFdR3WMtgEBDMuNtozV40l0UwbMFxrN3g3ewqb2t50CxQrDEI2MWuspZqhIgqyL1AoVNZi0d8m9SHPcKl301s03O/fcd/DZTSXXDT7MQvocpC6UqDDh0lmJmeKb9oalY0iUVoMdDRJ+eIDnjddFoiUUfk0hjO9g8A4gnX9BTA2KVxdzMth9zw6BNe5fFPnXI1Rs9G4nDjukyBnFlFMuNeGfaNzJWoCoYDuL2BnhYwYjbcvZ6i7eqP5DbXg76ROSC9sERylQyQYG6duZuQWE1BZkPpJF+haj7o8ZyNg3R3Jjpy9nISCbCdCnVlMyrk5LL1UI5daHlAYRqmi5JmqmCtbL/KSEhJonO5gE2SZcf88kLeFaF9P1yaKW+buJEsvSgRj4wrF40Pu5SQmeigdiKkjF0WEjnsDlmvBlm7Q2JM9XN9Hy9/0/cEKbK+M6y+vLrSoENHMcYuRVzuUmY/qSVFk2hppNnG3N+NLkB0JCBHXhDjjV/Mzk6/A8y/5BuJEgl6/vqqq8XM6nx06YfBlyduLy4SCXp8L8+oxPQlWVrN/2U1KKLUYCY6EriwCGmSGQxJFqsWs3Z02CErXLPESYMxz3duPR0SzcWCuuPjb97K5I7g++Sb6/M5x0Li5ib5ngUBoTRHgL3BjA0sAjR8usfNUUfFx8t4iHXA0aNaW9q6HlPUo/TVmS3GWt5jbOfcXCLUq8j4mkX13he05VBVF9jd2IQqqMHXsBPam6406NBRiLHBYET8D52lGX9WVyZdciIoACMPP3zx795G9Idlg0OfSPOBN18cmSHNxJw35BdxHVuKzcwmmZ9wuRtnLkSNTS2I/tPNMRbTdHYCOBx0OLCD3cLiBqI/3OcGA+sZpDQGyKxD26ddBiXDUkZicQkLikHAnLMGCIpaEJuM21rJV5kC0mTS+YyKG6nYilQsqCeCw5P5M3wGpAyGji7ZCWLSYAaQJCT7fQ0G5bPZDMTXkqwKJCWE9CsXovlyXFohqPkiVPnsKtearl/Xmr5hdfyNjc1b1Q+F4mebWaFUP6sOpHq9RKhvScbvESGqHZuaFqo3qPS50oN1EdUl/Vf6KjdF3bU3PeRSh45I0JOnM4r+8IBrtL+owZuvxuIdfb1OcomZ5n8Rjc0Ryz44HAX+/ojo4tUZ+0eOO7t7/Zc8wRmYo/W0v/N13+zinWQL8987vyRgtwC4O/dHJ5QkjXI6pqJpyESBOMRfmRFgnx3iBiOZNAkUq0BSTmwAkpRXC1YXoMCQLwgYzPlal4sXAoWFL0pgs7G6ChKZyXHZuqCSnVoV8Vs5Q0kyR8yUkEAQ6HDw7hyr2nYtmVMpqDZWu6h2bZmtVMOqw26qMXauiqRXjZSrFOCg2rIS/a3wVpXYPO3EvUpvsdNGhZIwTu0E2kqxqWjQvbT0qbrCqhOreZGr/6i2GThTjuoRs3r0hA4dZSiraenAfUoueDk72u+7uAJw5EshhuTgiCzEPvEnw4VX7GbihdENXV0tPxtfSalUi86R+s1mktDRwrtzrDUgraq1tGw52XEhDLQcYHFRAAnjx8JPveMZLrUE2Axy8sbmVvvawkoiW+ZKg4i1OLCts7SstDbaaN9PfJG5wncWEKR6SOuKsAvEy/pXWTW7KK5FIsUahikc/lvlY3n0RB3jHUpQxS+gEZvG4G31esldVa5+lQe3iirBhNuJCNhScIHGgMatRjFsddpVJlaO7QTO1PCV6e4JHTrK4OwdyoVSeN1AzJ31TTh7h0Ins4yH9kNymme/iOuYyy2uDEcaEf1hv6g0+MRXlC46HIj+cPhrj7sFGltps2LrcLm9fq/bdcxF51EMYO4K+UWSSUgyGWNVgg1Ef0i0F8/OCAK2qjJFEDBWsCLaHmzOCIuLKPI//bNyjQFkjUGgKXmXQzTI3hBrceRBhSCDRIylO8jhGpKRVIokxAvpAvIRsdr0t49yjQFVPSteqjEAqYpEyHtFeNSIHfIa7BrdsuTzPVnt8pctl/335KvfO6d7XWnQoaMaFP2hco1shQbxhX93NDr1oYBXskeG/1NwyhsK+48qhRp8I1HmHg4NdAOOJjOpDI0H6LCZTh7vPn6SpRTz1x0HDOwzOgHxdFfOIS+tEwn6InEwYM0OJ6V5sC1r7VcrSInssCj3LBhwt7ZkbhRlXBSg0EtpAAQEO4AgsbyajM8oYyXX8w+AetZF5B3VzH1KxAU2IzOxYt+EVOg+AbsK/cCylfjKemP/wA5lhNROP9y5Qbc/4t6RXhpR5a1VjQHluld1nWknFkR1zqpxHLv/dehKgw4dNSA6RySoREkE6fNOBYMRoiOBkbk4C5NER3ye4PWWnj4Rsa+nBY6cUJSOJhPJNEu3mZo50u6E6duTMWIWVjM0PUL3CTHyw3EEjnYWj5dh3fhIKPxkCxjA8HigQxa12RO8vMPZT4X9JQ8WdxI1pjMgWBD9gQ4DAuzrCIT9HbkmG2CUMD3UZQZJAHvLQXe+A7OafyIbMGE3y04HCSnhSm8oN1ar53mUIK5CP0iomSJMDWqaxOHD21EvHKc/XnZtB8I5ctBOP6yiYdQwUPXrGoVQOYny3kov7dC4zmzFvKHFJlFpII2BD1W+95rNP9v/ynROgw4dW8fsaL9v+kyo/Zxv+kx4gK97guPNDyLc7fqPn5n/wcXoXAEfwu0NnZn2XTqRTVM9++0vfOXCYmnupgMN5jtramd387Gnn1w8+//dZC6bANpg5Xf/LLD8zcBVsN7P0m0caWIF6aGNFtIJrEYsh7hzsyZHgP3hg/GriwINNlZXAAwGMmXCXbCoZK4EOk8/OHH+RulVZXIahLJ2xoMWksWWsT8c/r7ysb6chrqgNl7Czo27E0Nsyius77egkS6ghRtRTj6o9C73/IdUM3SlQcf7GrOj/b6LuKOcDA31OhUGpOgPn7jkycv5bNMv+0YK0z4d7Wq5Pr7Y6fczGJxcbVxl5aC379GRH0zxkVM9Pxm+UCZMoaT4gwNiAMaOR9o/eeLIyLAcPSE8GHrprFMZ0I5cW9pxOvz8p779Bd8F9RxLD7Rxkzb7XEl+KINApt1/OhH8f5ZZBfHPArG/+M+/TGf/1W9N6LofNEdvVPBKyP0U9ZeV/YJck7qIEIGxgXQZx3NvoDkcHlE+akkjXUdsNQSDyqKr7tghpWEvyE7tHEMtxEzVHrbEgd3j0N0TOt7PmH3tYlQ8eRLxTK9T/vOIPxxqP+cJqpS1vrMCnV6vW/SHQ143R5fHF610HToXnFh1bLDShOhr/9eI+w9PiZG/+XHcWpgqwOTuC4e8DtYB8bR4f4OA/VTYfwjgaFfLvskrt/9xeEV0I3pFC9KNb/4IZl8Lz6FoDMDC+TMe3/KXw483lL2FeFpk6TaOj/zhoc4HoaMvHPI6Dppo7XRkJKyu7ocOsZrB9Yc9U8HAVVljMFg/ehQMPCK6OzuLzP4m5f/LN7rojSQGE/IErDkipBU78MCjViEbAHLiyRbIFaGQpDLVRKpBYxCoSnSoHztNe2XuukKVCVhOu6vuoa9ist6DyRX2CLFUO+1jdzihexy6pUHH+w9jBXWm3N7Q0PHX+n3TZ8Kb8N9kl0T4iel+3/Qp79RwUUymCZOUzWog+v0rwWBpPoQ2b+g/T3/F989rSAKpNOYToSdv+kanMD1oX347Dev2FuKyGaHg4G49wNqd8skUFKp6QHTdvDhTaPS394TOPfZ8b+CymlXA9fD+d3+Vacis3i12N6jEext4wMTNsrqYFgfpmLZECCWWDC2+iWzDzFY9DwIGqaZZlSFXJ31XLQ07ehjdpp1gp80M2rvdC5aJEtQQt1mCvfMuaFhhXWnQ8f7C7Gi/b+SIX9EQxr7qCU7IN0R/mdYgKxdur5KhYfBMLiuD3LjwCgCd/vA3XKP9vn+4WSRrW7oeWRy/In+2NxJfwW6i8RNi7OY+141XbmYqiroH9nPzbv5Po4l0qtHKyroZc2PP0HdPfee0bzwBFTgHBvtvfzDxL1dTGJo5cJc7Ge5zYFnnxrL29crDhDFN2mQuSN9UAPF0V+R8WeKoAvGsfNTgEJFzWGnHtogNKqpGazj8HeXjdpSGrYo3XWnYtNtd4FXUhi35le45p6F6lrBCqM5Nd0/oeF9h7MURvCFFOxgbDK54A143XaKbyLmnPNkA/dnRfs/gj0bPRQCiP/iqx+MZ/N53IklsSjeRoMfj8Tzb9PtuuaCUxU5np9v7p93gbG/k5roJ7GL9b60/AAAgAElEQVTA64bOThYbP+MXEf1Pf0AgvgIQB9uvprxf4FaGjf3ZyTXaocVNmxkMAqaP9pnyGoNw8CHcLdisK+vAgR7n4gWfR9EYBBSNwfhAa6FtPRP/l2spgMwSixkysHCrWGMwIPr9nY5KZn8DgJkHTAAp0pKc8NFMiwmbpaipisZAkTCXyq5UQpn2Y1Jeyqo+z21tuvb9ZbrWge30p6AGw/t7JRKhNpQ7R/asBlCOTT07WtwW2gNndhTbdwnpSoOO9z7GBj059I/OVmn5o3MRBJsS/Th2KeI+efxY71D4awNnxLK2rb3KxdSi6A+feHvckGQNJXeTuxmYv3Ah+u5LgX+K02jh0IHVEZ/H4/EEJzCQgngkMLLSxsQERILBCPMvnV00YDRg348xRdR6pv3Hr0gCUqanT6QJTCtxWIxmcydu/Hw4kZeT0u1rXJ9jVeYb3LpQ6BvZB1YbgoD07oIsAq0P7AcwlgrUdAYMIGBrxgZkiASDEzHFBmBvprOo1FMGIMnNdLGbP8liitWCVEn7rfnPSs6Hmj3UAkgl6SdSilxfr1uoRHNz9tPdu2U316v9gnYUhYKkvjpE9cjATbkO25zMHuEuFEI7w2NHJ78HiSabQlcadLy3MTva7wnizyZw9B8Z8VXXG7C1au27eyDkdSP+7+GBbq69DUYaRcVK0eiiS7SBcS3BCgh3iFzKJ1bMYMTW2mZibi7f28QEa2nSGeJ3EQSM7/7FsxEhIcHqheGLLBuLil9uSOBoXiiXk+tlNTIFgPVVJImM3LiBhOzVSKs5PjIgsbpEKRNRIr7ExHW1hZAKulHb3+IFCkQ621sNELIP7lC9ydzcl6o5aGyV83jtLrZ6GK0ugVR7205agt1HvRSpPaLEVJ/GPdEntKywrjToeE+jyN2ALOcZeVF2M8yO9mcNEINjwNjgcBQmgp7BsdnR/v5pHFHZOPD1wXORriONoFgtfCNRrl5Tupx63jM4NnYrZsmksbzl83g8wQgTE1yOzEGadRIpFrJSzsGBBoA06YU5+WKHg84HAFxPitkTeUrCvC+dQgLspwLH9inC1io+fcwIIEEmtlTu2ResyincmNtMJDIlxSjX8iLbULgvaV/WXOvyZ9R2kjzfq3JvWkbfaYkkqXyiNB22UA/3xFbF2/YlxE4Iwh2VW7VpAHtBd9kF7Jxao2pnKmlQfYV3V2kYG8xu37vbW33H3aH+79Xi7MJkdg6yh6HoZOjsHZIZjWODvosnQ0oBiang4BjdAyHRjl2xFkRHpk71yQ6I8Qh9X2wHrg0Gp7yhcMjrYuV8yfs/2AgLN/N/p5v6wuHAMbNikZct+DHumJvywQ0mYDLGxE2AhRciBSfo1XXAaDEADqOAYEaA9cjZy4WagqFU1kpZS0O68B91Kq26sTQYs7YH+Vn5Pxq3IAkEjBo3iKOPWAqHUOtNAmGbwYybzdxUnlRb2Py5Ulqn9LP6/PC1i7c9cvAtQR1nVSWmUXWJtCgru3YQf49yTaqsT3WD06bYVaVhbLo9FA61T9dHFmnvrb7j7lD/92pxdmEydYFsNRgcY3a0v8j/cKS9gjW5eyBXMcLZfoifBD2ewTfbG1mL+L4+BiCe6X38hAiuYy6OtDrbj3D1pxH3yePO2dcuznDkaGTI4xuZhww/ef4XTe47FgP2U2G5PlVTA+bfuJ/ZN6JmOlo52pZ3BCwv5wVRkZl9DTJgwWLHLiAYMZrSiQzxl33jCRBohCabIvcsRkxARkUMm4WsGCygIqruZmuqMQilcZX71RopLdMaK0xfv5IosExU2oGkKr1pN0RUbpkq92pI+ee0QvjkTtWe2IPYNYmYE1RatAHVz1ttU30yqp+rtN8J88bOLf6OqqE7rTSMDRacV7vbp30e33S78k8ybzzWqtnX2FtJy82hyKWi4apAe/8F9vJSxp5qJwXt+0fHCh6WH88SAMtnqXVKeQphUR8qj5ea+qu8YeH97ACVeAaqE1D5LvNpl3wjR87kqke52t1T02oLMTZYtELBcRpPh0Ptfz8S477T/sZs2QhPcMob8neamH/JE4wQk6tG+EaiMHEdxECPiegCjb/3Jx85EksaWH/Z4/F4ghGzUSB5OeDxvbSYBrhzB6sDdxvNBgwdgXCfywgGsH5IBKPMPUS2RiRIxLEcMksZJMdv5wpYSgnisLyaAhAQ9pECGp/sKSlxCRYjgtxXnn5o+v/Ze/vgNs7zXvS3XAAESUgkRYuwDVDispHtKqycsDZR8Zjp3do97vheb9iWUj3qNTLJse8QqeewmE50OkHmDv4IMueot4PDe30N3rHHmSAnOo6ESZlNj091GmebQ5csaId1FFpxrIRLi4BtUKZISiAJ4oN7/9jFYoFdAIsPUrKN33jG0O77Pu+zC/B9nvf5tEuBGoUbhFwRqVhTSlNb6eYQuiEAZAVCutDooH/PrO/WbUJzwRWB1xxYGaqLs6tlLSUqkkD7ZvC/owwqd4iboxgb+2/e0P+N7HWdhrBfVY1XRjQatdlsiIbGL/RM6FLu60ttn5FfIEAsArAklQAoOmF6WBog1jemlOOlYkNu+JnzPSXolGTpHM5OjNqAcGkiitt5bKlG+ThFxYMshw7Fp8InlK4qPubWKrGUkrH2EUxtyMRFSh74XlzCivS+ZerqDwCiL3/JNWXzsN9yhP3MD69jc0C5ZHakPfSsK3gDeMTjge/8zxG5ASNwaMAeeTeCtiOW2K3fG+n48eUtbMbsbc2RxTSMBA79sefoxb+Za04imS/tjDkrRDsgh+V1tuPWBjIE0CSdyzs7cSCJa5vZeW04lMGagGRen+fmgc6dee360iqY0LKLg3/s3bnoXS8/uk7tHQraSXQ88Vsbr/4mW8CqPH31IBPMKST2ZPM64mGfl36o1dVp0CPCS4QrVoQ9TVysby0BnaxW8fZuu+y/Q1Dpj6q611h3S4N8clQdLVVHVZsNoXGGOYezolTROI3Wg5rOI3JJ4pUNDvsZf1hkfTwUFq0W0dC56eFAoOe8PBZuNjA8fU7kUmPF8IUgnGezwss2etZJ6eNeL0uAraeXl4IGFbZ8rekzHJ093ttGz9D8cqRwzbCfYXxLTo+Sy8gyLxVjtp0cpriZwncphx8oB0SXl7JhCnlLFflSNsFPTfHgfOIX/uq4aEPwcVjhIV8G8NYLjBjhyP2wkI+NFKy/7QDgGKIXNsWXHvYzX/V+VTHFNvqipz8lpU/yN5ACUvdaI/MRxIHYtTjWfjzFYzGGjY7I4g6QQSqN2EXf1c5kp6K6odUCAGQKpmzwojKQf20LZhJNTTAdvUu0EKyt5TQGAKlNQyyR0xg6SBAAQeZrDOV8+NtpxIpoDM2thTYAifFi4Qi6IyTyNqR1SWOATo1EUK2TzGkMBWGgNePa+dLpN7Xjtp+zdeK2HMf1HLKrO4jXJdr0js2Q3Deu6qs0REPj2ew3T2/wnPJPTx2VJp6dAT7oGg9FbaMTbMGJsi7U1CPLoqIpGoOj9tMe+GaGWJZlz+L89PCZk4jOTktGddojjZ0JwzZ6pnd6Nqq5okJ2irCdHM7XGmyjZ2jOxzC+nECvhCVxDbFOkVIn02LG4VYYCcIzHNVjL3wRDjfLshOjyuvR5SV5oK2nF0vLxffi6Oy0pGDYenr56VlJlTqfXarYl+L4BhtwdgMA+KCLYSbhDEhhB9TICCVdPje/hbWOMZZlA047FnzPfjdv8XYjNj+Kik/h6ZWqLVzpi6z9QYBlA04K1IrPHwYc3/LI5RwOdVFOz2Ot6LOC9rDeAROoJwZMMHcg0XyK9fQDXVYAgKkTOACqG7QnQFsQiwPA79JURw9AdcGQq0nQbIKlDYe7YMpgZ3FDlJRGAmSeZyFtsecs6uti0IMBRzry8ikA2SUCxXyjsoFDu5bE39lSZmkqIgsLak4DBAEDQAiKSyqonCVmQOlVUaCc4JejOM2qhVJ17mV97Gwx01ZZmVHsrk5hc4ecmAu4rZek1C/pq6iVVBa1O0dqoaD5Dm+7CqL+RvSwVFcdPTo7zdNnROHicLMOAJD+J/8bovxYFo+Zo4rJIf/sSbfir7U+1NQjy6KiKRqDbZg9t+Q86wYA2+iZXmYmMjq0jJ6TAADOx3AAKGdgAoC9BzMRjOpdkQ+6GLEBH+UMTIw63CzrrpYlhy18YXo4wE7YEPYzLiYoeRVKP340NO5bcgbcenwhkWUePeWHSfoe5QzIXy78DMNkn9JW5KFyy6xQmm6eg49PsF8puGYbfcGzzMzcZxt9ms0uf25qDeBcjORZKXynoxNsj5+ZyfLGuhH+JvOf5la3tq0Ajj7DuuFn5m3OwNjJ2X/5y+CNfs8fhM7P9lluLsZhob2PL3lfN+X+PC0dePgvAj0v/uXcCoBVAOSQ93fe9M4nsZPETjLboOronw588Mq7LWjbQCwbVkkAAgxAWlCY60kgs4Nred4KEAQy8uk8I+cHpCCAJAWx5OIh55OZ4I/iasu/6EogYGxKpYqELgoAhPw+EpqpmEqiECAkgGyfT1MnkmsQ+3AKentSQJBmE3cdEj66oW9OpSgMcpBQIDPKluAVIQjlJ5a2wJeVnfWVOiVEoybzVVC+Q3Qj/dAZuanHCyC/w7K/iv2Bcl2dLNXZsAeNI2gOij5B9FDhzcgyt9zjRt7OXydqJUdWTDx3K+u7LxxsGz3Ty1wIj7odiIbOi8J6GTMRwC5Ogn98WTQiRGRlQheT2sJRB/9aLAHgp2ejo6M2h5tlT4fGXf5wtiWDNjN5YRjq91AIe4/aoaIxS9L3wn5mPBSYGIUYyMCyoo+A8cvUtbmKLi+hd0jHSynyULbRCXa0SLRM/pSsNQsALPTY8YuuIAAfk21PMe99Jf7Z55xrPpeo2JGdd8fxIQDsgF8H73OJczmfC0DfUyNrr0ytAYmZc1ELkIHFgMROVnq+98o8AKD3CGLvSzJVAMiO9OE/+PON4PdkgZkBmi0Y+t8f/efJ12TtQDDAsFuY+2Ac9D5yxfvPKezuQhDAB38kES1A1pVgOECm1jOiuCeVtgay0PCQDzuJiOq+apmk5FDJaGxN5eMcatIYCFHzKnZ7KQJUHiCkQgkxIGj1NFKiaolyG8VPadwhMlInKogKrPy5ann8Ej+bgh/V3qlo9Y5pKHR2ywkIYT/DiOZxNqDbOV8HalWsW2aKw52tPihF7RUOFt0qQzMMwzDncGZ42uUP204OL+U8pQ73mWWXP4xo6PzS8Emb5ooKK72I6Oy0vrBunSzJtng5vqHk42crL8pKS/570IKtpzcXkbC8hN4eW4lZjiExfiGyzMt+GccQLTk1in8pucCJyt6JEtHlJRUN9RTb6AQruT7ocfcT7hwpsUaUdz7Z3f/Z0Ql2jAKsxzrJtQ/pobtfv4ZFZfjAkTEnhWNWLIa3P4cTfSZY6LOPH0bfXWgj0GSBBei0AO3o7ETnLt5ZBtqPobMdFnQZsbtuHz55rIME2QLa4zkCGFpha8P1119LWmAaeM5JoRWSzLV0SD4R0XORmvNycSR3IAg49FSAtgAA0WGDaJUEAFhPBR7N9t7ObGRk8Z2nBJSpD6nWGDRhLDIdZTWGWiGU0Big2+hRDqUFj9q6rtP3cWc61O986HeO6HnDolSu+rvYI/+I8ke1d/EodVUalNFuBal30eUlKLflUu7telKrYt2KpmgNFsP7ROk4MeoQg/1so2eHp12u5TOirHS42aEZxjU9fHbUVmRFx2kncpEc0dC5oD6dQTdLIhtsYHjaxTBM9qStyUxh5odO2HsoTlSVciELSkh5kdI/pFAJew8la0vhGU6sw1D8S1EGTlT0ThR3Q+eCfC60Ixryh6K5KZJ9QWxRxTAM880Z5YpijAobcFKU0+s0Bi+Eo6G/54HY1bUkcMXnXcyK0M5DOGSE6cbkj1bxQQzgL3G4vJhE/Mo7MiuZTcSBtTiQeeLJDtx1CBkAq1extoE4VlMQLCeWXZPrQGYbnM93EzAmsRjDwgKIJJLzzwf5XHEI8iDeesHFxWUZaaQG7TCBJHDjR/8ntw0DAWE9CnGHAQDELrpeE3MwBSQFtfjuLLBNivcrLdpEEBCzRwRNvaHe9s9SMJhUFaESE/5yu4TmXqzTYaGJsvKgCod6dUqGuviS5q09xR6pR3WRoyKFingr+2u5Q+wuOlmq75+nbXTCI3mjQTkDEw6EZ7J3zjrHXdIdmqb45QITYHR5CUs9UThs9aVWdqQqF7DslPwn1j3YNjrBngyNM4xP/DflDLBuWwkittGJAMZzQQweJ3xyTENxp0BF/CNnnvfNhN0Oh+b06PI0D/BiNEbJxVWkPcuMiwlmv8ACONyB5fG8KA0bgNyk3LTiDxVZ5pXOCaULAfLKo1rTkU1ZzYZ2iAkaPChnYDRvRVj7qdjhbG7oM65g21hhyK5r+QzrHggvYxm2UY9z2vVOL3W95+zEaMz7p95fpJAC1m7YKUvkRltXW3K1tx/Rpda1NgtiK8YYt2DHYkxJER3xX+JvAva/cl1V/PGagcThgz3kh1w24yIBIA3a48HL/4n7QDoik0BKANkCcwwfibEOBEgBu0c8w9e9fBK7BLB5HaWdDKIPQnYUEIAAI25qdoVQ0zGakNLqoC0ib3eSF2xFRuyHUeyor+UWIQhVakZFSCdVT3RovHTIjk6/w75B0xZdizugYHBdRJp+g3m9HBl7YZ8vG19SR2q3BbrcK/Wv05AX0ViqskLBLO2U/PpSyyLsH18+Ld0N+/1wl6epnPJJgaL6Qpk6DXcsivGt63l0ffWFauWr48zLnd6Q1xoa971+PbIYFzUohbZCjzmXJpcformLspJ1gqYuc3mWoq6+1tXFDOgv029McnEAhmZTesdkQjxP1naOjH1u6r9wiAM40ocPV+zJViNSMcS3JItC8z148AFqaYnf5on0Yy98/9/boqH/w/XdD7UFaffICDE1tVLEA0DgQDturUufSQEZgDQZMslCMU4QMBuwLXLQcaJz/fJNQjM6oTgMRGknQcUw/P4Afjpfu2+BeIr94Rnps7JOw89+lqvTUGLX1Mx9ryhWTg21LlJ22963SgZqzmusrFAXzmskUnZ6FfSVU+4oTahS7EFFSNvoaZyrW9+C+lLLwuE+i3PjoSgQDZ1HWY94/pRPDnIJhoxvyVk00ewORdjPMIxP1XtCN3R/9fkrTvKmfmYAABBZBCAmrYpVJGkPywacS5OvbwH3idEbgZGDAJQag32gC8Dq4hawA26Se9jDemh0Hjj81NigrDGIDbgH6Y6pXw15HhavXVvrTD78JfbZXqxt5XwQOx9gjuNXeAitwvaPXQzDuIJFNAYAK1NTMaAJVDsAWJphyFrnSQMgZDUGgBCkU71aYwAgCFmNAcDWRwlVPCPResIq/+NAtwYn9dUYAKTroTEAEF6pabvRtPGWNYyXdQrcgdZs6PCk7Cfq6NSQv69i31rpr0OTk9IE9TMmL3G7UHdLg3Tg0me+3mdqWYgx8bozEaqc0sDHGnl+jnyIPwPtIpcosIdFX/7S+NSxrwUeOO96fQtHn2FPL3/V9YMIPR7oOe+a3kTvs+zpZfVKdmfAg3Ou5Xus3MxGNkkRlNM7PO2dz2DhmjSO9rBue2j8HB7vDV56G1sr9DOs2+7/M9c/bZPN6LoHK72eQM93/vpiZENdLdqEwX9jv/pGZG0b2C0ffUh0/V7z6r8kVJf3PG5xT1AqA8R6in3xaQBVWRrqgvqetquYXoUrQYkaj+ZVcF7WVFMj9LO0pzaefTMglUDdQ44K6iXcUdSy0C5vUO8pDXzsIfsdls8Ees5LDq9oaNx1ITzqVgVfaqR0RkPn3qS60aYYZeu5F8hLCrKNToxNM5MKtYH2vDBqQ7gXLBezOgPPLLu+8wYivZ6JUav/B1g41A9qIDBxcnbcdck3/vIIeJ7PTudmwvdf5bYBZBJY4QHe5+rraM6K+v4jWPgQrSlsCUASc5yqsicAgDDhYAobBa5tpcZAdHQI6+uA7HNuJbGls7sVANJmzkRV+oc+1EdLKc4r8ZSkMRRDRbbo22gVqN0GjvqlR1aUBFj7ivXNOfx45YvK2KMMzP2MU26ggY8tbCeHqWnxU08vLvnECEnru4ADQNR/xsW1dQOgPd6Nv/EuzWQViL6nnMZXFKWoov6JuZaMVKoBZvQCQHRr0wL7YyPGqctbWIxlC4ABIIGgywdku1bFNpKwHmyNLcgRsfzUFNAKbEkjON8kjEBaIVkX13cAA4l0BgvXQBix1VREaBLoGn1q9+Ir68m84tby3RxNYV30Y8hlpirQGABUrTFg7+0awn8LRc9oGBSrO/XeCTJm7zQYzcDPYqvs83u47a+9OtTryyqWblP7D3JfW2M30MDHFdHZaYjBE+EfckiOBMQiDRfFKJfYRhI4/KxcfYJ/q2lEtEQsvhLkwflcQV7UBlzcNrbTGPQERjpBJrC0CkRnpz877jk2JbauoJxjtJjI6elHRqoAvQMLDgMABMQW5nJ8dfRT9BP3bgME7HIQQSorWZtBewInDACJ9C4GPYEjBISUQmMwd4FAe67VJlYvvtLWAYHQyqJUbTXN+RdrdLMaDTDuj6OWsP9ua4m7/2tNLsh6+dQ1P1eBKnI1K0KB+/92ieo7J+ajinAH5V3NzzqJV42KwkEaSkMDDRSDHOHI+VxBnpeaUlwH/dWviGLlcL9YUsLa3w3LgljJ4aU1E2A7tlHQD4qA0QLaE3AeQd8htMH2le94HzRhaxXR2enDOO/DmJMC1Y3enieGaO58KBqeWaDoz4kxkfcjbr3LAVz9dQrkAwEnBdrjHTAB6ws89+r7AiAg8hEsHThAACRIcQfYAedzXU6DACBgzue6VhCumFiFgI244lIXIuuAoGGJKKwp3DdyRPyUDaUUBKB75J7KBZM4I5VGqqy/uGLauZn9/dnPQuRnWcOMsbWjsGe4UEMYZB2l8m2UwQWSqe5VE+7kMgzFKGt+rpSTuihwdX/MSrlqKA0NFCAaGh8PReXim594mtHQ+Hho3n+GyYP/5wBojyies1Uh6TEnhU4pwcE2OsH+xUC2uqQRh7sl+8DwYQwe/vu3jOjvF9tZBZwUYELzwwG3I7YQxeYXAj3nmW/6X+LbcPw+2EYnvtyTwhsXF7L5CHL9bZ6bAzgf41sAWluiwDE7id1roh7j5QV09VP2gQ5pWgbxddwSQOwiUxCAn68CmPsOSNflKwaYCABo3gAgtbXqyDc3CAAImAiI5ZCExamr4g1loYPrU9fFD9bPavalkmDIk//697/qd0oBCwsal1Nb67kUV4kl/unvaoysdt2Pp5Fclkx1N1TsteVjj3B77SgiKlK2SjuJatHbGjENDRTANjoxAQBsHfNV7mSattGJCURD06DHFeUWuMkgAJ8LFtDisJ5eLHedHKb+IVvNOxv46PL3BHqAG6t4/x+jymSKzRXxrzK2EAWSiIs9sQCITSv4CEgxfIGiaSO6t9+NIr4D3se8Z6WGvTbMAx19WBfzOsEHL4RHP/vRLgTi0eOm164ksZYCFjQSPIrubNn4g8TirYI7iTRgANLYyTa5gID1vHYTREYQACSRryXItAmpvKOUShl7u1TUQi25kfVO18jRy5IduK9OpEucNesifvRQq28QXAN7Ck0traDUB3THJagnKsmqP+tEw9Kwv4iGxgtOtCWOyQXFsyu6q5+d8fFxuX7yeB5DYb/yn3k1n7O3NR6iapryu1E/VQ185pkiStN5188w314GaA8bcNotzbDQzw0kMfuiWMmCs8A4O40/epzmzvu/yWQjHZ8Y+GffX124ho0MduZc3/T7gjzeextUG/gVMY7BO58EgFYM9ltAAMbPB5wUBn/HjF20Do50g+c4Hour8SSs3aDHnELrcOsFxrcArC+2doNyjh0DCMO0j3lxRQA2X7uSOx2b+gbbxU/qv36SgBEnrPKN0nlwakFuyJkaMvJkeQpB5mabSEEASeZtZc0W8UxigOXEcfF0Yqq44LQG6iv4jF2FeyBhrT6/u6w/W/NzdSigVoLgnXBK/thhL3wo1UUk7LU9plKuGkrD/kPqViDavJd81RaMsskdJKpGdHaap4eHQYvB4rbRs85sswggGjrP0Z6iK0RD55ecAbGBExQPUT1NqR+Uh1Z1+6iFT910sPRTjvZ8uUcaiG4Sce75+SQSK9L0OOcN8nxwkjtygxNjEamuzg9f/XkSWwLQ1QrSMvJl9zPHScRWNhIA1Q1L9u/dYMLWkbWFHQDY3QaAtZWEkcTW3PQK5XyqDwAgILaC8GvT6H1r8p8PHCIAYGsFfHDyvZYDLWQa2AFAHLS2kAAhhg0mF+ekRAfxz125w2QEpFovb6j3AZ0nz7RWjkUugUJRzymZAZDJVzt24qIekkb88hXxVrJk4eq9QNn9NrVayJIQG3+1hhVvo4TeozhHzc93AsE9io1AhepdpV6DEj+P2+WvqehH21AabitsJ4cp+R/yyV3TfpC9O+73j0u1LCVLg9LkkPscDY2Ph0LZSaFojkBOSbGNTrDu0ZPDSz5pvm30DM0HL4QBhC8E4TztyBkAfJzUzFGkYOvplUbC4c71vqyeZjh3TfWaauBTL50t8CtSmgPncwX5yGI2Vg6tIwGxt6UFR63AMTqxgQ4KAPjVtS1kABx+0LqagQFvzr760ystzQBlNQKbiJMDXtY7YEQmCVy7ihQEIPOOKxjFzVtICwcHT6xa+OAriwBwtAsEsH01ati8THXcutEE9HvGKAAGe/etrQwGT50A0HQztrsLoN1qAprbC95V4R/+VrYyFIFm+ZivtTlUvF8RlYclkug0SrNaDuTo1BDfqIABGikR1Uil5uEHq+WhhPzQ+X41KdQoHWucrl+i6FyoCr1KTVn9WR5TlofSX1NFL0q/elH7t6D5uVLimuWhKuKtEdNwWxGdncbwWakFhm/JGWDF4kH+cIvlKgMAACAASURBVIERQb6LsJ/xwamLOh9cPsOybkRD4y4XQ3uyny+ER0XqinJEYrcqwOH20Mz50NDy9JLzrFs8j4sFtgq6OUSXl8QeVgVlMqunGV4GeEDqiK7MequFz4guOvd3roEaYyeemPeOSt6EHLamXMyU+DHRBjQPPUtzL81KN81WJGKIzMcAwLgS/P6xx4zCv9gvv7WO1BZMDz87gBhLoKUNTYPeh8NeTtRFkoglAbSkV26mO9DVhNXWwQwWmrCVIZOx36CLhJVEbME3CQDpa+/hUBfa7vuTPuLyosGMVALYiOwAWvUUQKJlF8km7GYUQlPATsljfsWnPp1togyP0OlfvoHVOIAM1rI8bMthFVpUDM2G9I6+wAcSZAYZAGm5VkWNsFSZcqnpdS62EWu+7rIUqkiyr3p6pZEQ9S0GUCllTc+9/pdcjEi9UMvLkQfrsU/ofyJ5WKW8NSwN+49ck2UmVz44PJNtomAbPUNnW4Ijd1cyqcNx2klBHyjnaQcg2jPkzz292cbQkiIiJgbkVnScdiLoC/Zq1bdRcHRhejjAsiwrdq/IWjpqoSl27mblYgfyUrXwCYc7S64Uncl5E3q7pCvWUyzroWEF5XzOTgDtY2zAKbZPiMWABZ+PQ6z9GKiRESsSMYsdlNM70g7sEP2da9yPd9s6HnoUa4gLSM65GMY7n8TWJg73AE0gALSdclI41kHCvD2fAtaxegOIzEUiWxkAGSQNQBKxXRw7JrGUMdt7WwCgwwikEtqVmUhY6CctANru2wYOHL9LAADjvR1ao7VAktmYAwKDp47qm2NHX1upSIX065yoMVQCpcZAlD7ZZEq6PAh0GkoZMgiz+u5qGfdE1ac6GXvkvKhIIShxKC9xpi9BrdgSeti4jdD5Pe4PkwXmhFr8XFXzXHZiQ2nYfyhiGtiAE5KNH1mjOsP4OOS79aPLS1SPqmxxjchXUxQ3bCeHKdBlWzmJFQrgcIuhGf5wHWjuBZ/66PS1FQ62tgG466FmYGOScQVXFHetoJzev/X0Tk3FAMQj4IPeKYMRxqPMk3aYkhkcHH6SQpcRAOUMeAdMaD8CPujl4gABpC4GeaA5Y8rcxCoSAEyAuX8Q1MgIBSARA78O0oDmZmlFIRGZj4B76adJuapAx8gxI5QQMohfb+smgZvvQsD6Lz4CABy6/z6YSZAdsMjS1wAYNayMmawAJkjMXXyv4K5B9oQQsJ56TqoIlYlgcVOaZUZhzQOAyH+xfVYYVWNKeyiEWlItBKylS1pEdtWX7BNPFB9f32DGOqKWwLpaHkqPSlH2ehVvUk9shE6lRKdupzmsvjEf9fp11f7LLEGhoTTcXth6euXPSmUivy+WraeXX9buEnC7IDXIFMMGlE9xpyHsZ/zhaGhcEevAML4rGiM5HyNaBWIXGR88zxwFgA0DLLK6Zjj+FA16jN5EJOjKejrMI6L1Ip1CasG7/KXAF3Dzg9c/whZWWylQwydt1v5ObFzrHAkEaAsgiGmLuBpDMgUA5jYgCSQW5vof2piKAC2PBQL9BmQSWFgA7fH2kTA/MAgTBr+AhcODdqC5Fa3rP76aAtButwAmMwDL4DEs/EOCADpE3UNEjJtDogmZdcRl6ZsGUlBGQ5DIE9s5OU0CBrSTIIC0XMJBMMdmfyZKWwIA2rpJEICQEJ9MKvMAs8UACJsH22XKFs9/fvG5TgCASRGAQGDvi0MDILJ6kgFWuSBVUq08WQsv6IRO+aHzlqZRWs/n6ri6k1HiqeWzeOlHU4rAur8rPSaBPf0WavwlVMpPQ2m4vQjPcKINwSFWAQQ0chvz714o7Iio8DhEZ6cLbhaHY4iWjAXR0HmuciuAw82ygeFpF8MwclfHWmnuBZ9Z0KdoUKJ7YqQzhrfzUzSOPiNqa94B8bDM+XycaBvo/hOPmNSB7p4rr3DgJrm2vhTtYVk2QJtzYUEGwEwH3A7bn/5eK3nt+eAqBp+b8PQGXYwrGIOF/pZYRTLvD5TqBqiDm1S/6BlZuMghZTj+H/69DQNdAIGuU6zbPv1BBs2/8w3Pv8HcRQ58orsTaEJff5z2sB56IxIHkglLM+JXbsGyniDQcXBqigeATlke5iormGVdYVXBSJMRRgHUWPbxZewCaWwoYiPEQg+IzIoBBIY2AJsrmbznkhpSJOJpAMRNMX2DABD3Mczzos0mqQhA2CdZJmSzStOIKUpNqKpO/Kz6TOYC+VHa2qx5DtaUQMXEkk4Ldu0JHRWdXMsuVPr0r/+1qNdV/lcdh3uX/LKnaTWliStVqxI/JJ1oBELuP3LtiADQHlaKVXB7ZhixCxHlDLgL/PS5u7TTSS0V3DztPO9yMUGAcjppLEMfFCvSnuIlkvJ6REurZOMMbaMT7GjYz/gUcYW10qwzn7kB97k9H0l0BmnqesE0H8PRHtZt7+/GQgSKUMiV4Nf8J/+m5yUOsocitojY+VfdjgdhAOK5MEmqBwBiG8ZkE5DEnI+Zoz0saw991fUDubKTNNQZmBi1hf2Mj1sBVlali49vBS9d8YrdsACsXmSYi7BYkLnI+IBuCiv85fk1AFhYADUQdZ9+rIX78TYQ3wF2PgRwA0C2cfaaKCStT9Gbr3BSWEFCGTnZYoDpXmxcQyoFAPykt1DnLNhJVA2lU5tA6yFs3Sg6S5AmFlSl7DqG1au4I0FuaHhQqkUVwWt7DUHVZapAualURVCf44XiFaVKG/n37bWU4LDEsEqDQ1FhPGl1S9SLjs5ZhPAxtVh9aiF2aq61QkMVKMhKUPyz4E71NPeIz5mhQM955UuLhsbP4eyEmIuSHaxogb0Jvs051hv8u/ewudgaH3Sz33B891nmvx8PnHfbFF9B1H/GxSUw+B/YbzhylF79S+blDm/o2cXxczcfxNTGGXZoRrLFhP3M8/+Cjd/ysN9yhP2MDx729PK46yed9kj7l1g3/MwENzjOfsOe+5LFjA/7IBWZkwQ65fQOT3uzjzPvHfVGv8g+8xHz0hXEYqrXYQHVCvzRc52vPL94N9ZFfaLviYHIq2tfGSMmJzePIfkR1tbyZxmAnpHB61NXSMQ3gI7fMq7/RjyREwCsIwdjUxuAoRXp7co8CwYiWzIyB+MhU+pGUnO4LhClWVApOuXIPcL+8Kz0+aGHHpKvv/nmmyiSsVYF6kWn7tCTMFlH+qVVCrUyUdHqdRHAamr6ad6x33ItaLgnPhZQFJJ0TQ8H9l9jUEMKamAYhvEtOc/WVe5XjWhoXBGvEPYzjP/dTbzh8y2ktJNWHO7A8LSL8YcLAjPoM6Nd1xFb7DCQW+Tcd/zfZN48Phhfmo0CQGSex1sBhvH/vKcVIMRSkheugP++azwUxYOPULZ+K2yjZ4d/PksAed4lbG8DCz6pHgXnY1zTw2P3zkdw5e2Xn/FxlmbMvfBy1NbTluXXxwEWHPvDCZZlA7QFnSNnR7EQBed7VqIoIHbRz11GLGZCKwZpe2c7TACsYyzrHUjiRhzXlwFkNQYAi6/OJ8FPTgrdiGW6CjUGABmYlqfeNyAu2iVEjeGeJ+4lIABNnY/3GEGSMCRLiusD3Wr5o9IYAKRr0RhQVmkppzEUpn4IvynlnbhjQwTqkolQaUrI/qQ/VB0eWEePwB0V9Hp70bA0NPDJQdgvR1eIEH0PtKfA0qAw14T9zA+vy72MxJEFQSOWxz0PJ2ew1L75wdSc3FDBDPqvAphwcYpswvYj2LgGAJbfGYz/Qmpgbemn4wuyZ8JOWSL8oX7sLCBnFaAGqegcnxRLPsiQTsgmYFc0ZiD8dcaX7bpkoUWzx7x31PveffbVhYh1aDD5y7kOo73TFMl6WE7YDZejisDGlse8p37xfwWNjrujr30oAEDf44OLP5lTOvZJIEPCakQsVcEZXWlCIAlklNuKuN9+fDaafg/7LfGnorY0VIoSh906noPVJ9oaj+YFc4tVBNIcrH8VzSnqu3fCeV2tNOyzpaG+VpNa0LA0NPDJQa4mgwSxMrXaMCNbFcIzHA5/kc22sQRsoxNiZ8uAk6KcgYDTivi/vrjUc/quram5JjHDJeCkYEmA87m4OCwd2YtWSWMAEP+FWBHCO2BC/NeSxmAF5Qx4hg+D2lhCsk9mhhqb+MZEKOCkEjGY6cAYZUQrAPqvWZZlx6xJII05H+P/7jd97xha++wASCDOiWYe73wSht9+IeCkYjNza2vgVyLzuZiMy5F0Xsri9o+9wVgckdc+JIH+U/3A4qWcxmAiICVNZhBLKDUGU2vxg1bXYSDfhJDJxjFIh3ihVo3BtHfHPK26lpuFdcyLzS13zi59RN67yLg7Ni9UhJ6ovVI+9TvgiXR+a/WyS91RX2hDaWjgEwGFAyevd5Xkj3h9KzdsPBQNz3BUj1HMwwTnk6fkymOszk6j9x2XKxgDVla2WtECMadfCpyMA+gfc9qRXMd1hP2MKxgDAKobfV0wY+WS6MoQAMNIgGXZzxti4L838eoFHvxGHGuLoD0s6zkG8JN/9fWsyyRx+dL8Byk0HbDg6nIUYf9kczuAQU/A+fbFOWM6vfVhBKI07zgG+4Addgvw0UWm0DwiggAAkgCaQYi7jSGbHZEGFi4uKIYBSArF2kMkt5RR/9SYqPI0958AsHpdYwJQruySDMNnNMo/K7iSGCuOWjZRUmsH7O3R4Wy7o/bxekFPVt7+n3RLGGlKo47eE803UEC/6ryPevFZomBXHdFQGhr4BCDsd0kFKnOFphD2+yCWvgiMrMXEGAFXkAcfdPngObPsnR4OSPYFzscw55Z7Kbkaxuavpnle6koF4NbkhR7vYHNCbEth77cCJlArk6+nYOsGfuiDx9tHwnjs0fUVtJ5y3gXEgi6GeSkKoO1uG/Aff5EGsPurHyUAowHWUyKf9s92wmi8umB1ekc6Te3Hu1an5hJA/FZi4BkxTGQjAxOF867gipgcmM1UJNevIjIfoR7qNoK8C90UqMG7AJiV70UAQaCJBNJoEve6tFb4gOxW0Pe2BX4ySgDAzsJl+aK8SzVrzCgt2H+tXf5Zf9C3+hJp1LlrZjIa02c5jZH1x16LtKpJif8VsKcWfvupSVRBv+5aXcEbUMd2VLfcHhV02ju9oZFy2cAnAA63nIpp6+nNJZ0uLUfhsMH2le+wXxGvREP+2ZPuURsAcYqUNDozNOF2hMGcD52eGJ0IhMbPtQ3iratSWsEWOJ8LACzt1MMD4DgcMoKXUzBXgK0PPV/A+bdf44G1yaB0vb8ltgDEJhlmUrwgCIDh+HHzlQ9hawXemLnUtoYUgOTNj67FkxtX5NoJTQYrwt+U6kfF53jAbEXLKkyHEFsBsq4DavhzR38wFb2FlY8A/iMActCFvGIqnRuPYskEJhiT6ooFRbEj7+FZckI2i2EHJImMqnVkUVJG2z3p9z5Q3b/nHuKDwqsECJ0NLzIp9TDNJAuyBcR2fldwYujCX+tZo4C6Vn6gzszD6nILCygXS5usJaahLHu33b+OO8nZv3eoVyqmHpRdqGFpaOATgqwvQu6SKWVG5Lf2jCxzudqahVMc7sDw9LlQNDo73XtmSFH/2AT0e1hPP+IJnrveBuCGKGC7B+0ANTbWtzjp48Cv4GAnQPV3wTTgPWVdiFpb0X5ErCs1cg8BtP8elY79PI7YRWaSR5x7G0DfmHdgc+r5+RSAzk7r8XagjUz+7KuMbw4KJGJYSyO22ilfIY+N9QZf2gB2dnLDCJN9QFVxvM2Kzuw8DY2BADQ1BnNPeduDglx2j1FpDCWReu8DrVVUGgOgt0VWEWjOzRRoDACEGXV/VC1yWjZztfFgjwIXyh5Pq1hXZ+rEPqRLaKKYg0DGJ8NJpH698pW9+EJLTCk2vaE0NPAJQNjPMDNDinhGEWIcpNhV61yBHNCeYhs9Ozzte/EnvUMOh5v9zrecFO1hWc/dwNV/jDr+YtC4AywsAJ0Dg+g0wPr7f3gvTGLuohUUPYibawC/YEA3XrrY2r1l3MXGCn3/Rxw1/OaHArDxL7x1tc+K/n5xvaut3TjaNfDsE53Gzi5qZDC1lrTcj9bNTH76gQiCBJBZA2A4/ud2Aplr85vA+nbeICEZmY8UOho2Y1hbK+4iEAAcaFNdJhLLmuLfoCZ0UBUEoKluEK0554XCyFlLc4lacZe6XQboMk3QsrjDg/X0Q6cecNsldEWa0F57TwroV71c6RSSYu9cqaGK/+2boaWhNDTw8Ud0eSmvc9XSclSzGne5KUA0dG562ON9we0I+5nxl7/9A57zMYzvGmAbsMH2RA8J4+8EWM/n5uewlkbsoq/N8zUa9k4Tjp/qlYMgjnte8L4whhV0/BV7qp2b5MAHI9LfdAxrSRz+YmCkEwYzwa+A8zHPvbbZTMb5qbk41ubmtFs8054zbQAIGM2W9JXvRQRgZ24OkUWVYLfQfRptmLJH7eYDmmEHtzbF/xuPd+WPL9A1SHV1JgIgDxfqHBnN4++WaBMhiJKaAgF0HVDPLyKmNMo30p7PqK49wrKfVVE4Qh3oVF001b05nAZKyJh9Ps3vdTWn24i9y0+pL/26mBD2TW9oKA0NfPxhGz3rXJJqN8nxjIrqU4wPHrFEZHR5SdQPNKeE/a7pYalOlWOon5+ak4owUN042gXA+sg9aP6F2KvKTlmAfo/b4XB77p2PgLvIiSNpj5jk+eBwNxZ8/vte9PQDJED0eE81A8DaGjjfufdJIp2Q/sgzt5LxjyQfAwEQRwKsh0Z/PyA1exr0sG7HI7/bCkJAKhEH7HYTgGMeNnBQ+SYIDNLU4XYNA0H/2GMmACSG/og+oA5laj4k7UOpK6sFtwTJxiFi12IpnCug+di9mUKhLkAt5glbd9bOKjOsoQsIwI6GN6FJs7pz311qApzv16prrzPM2yqaH/aevV+1B3bqrVRWYwy/psjReZrfh2P0fob9V4c7oX6DjNtuhhFR4w9Dz/RGIGQDnwTYRifYXJsJt/g/h5tl3RVNycVTIrq8KYv/aGjcNb0chQMw4mHFxd4vOgDA8WUnNScWlg6Nu3K9P9pAe9wOwPFEM/Nq8u5/N/D026aLF1OHBwXLA2ePhVxzaDvF/ten571f9M4LMHXQXwu6jX/JeBej/3fo/C+tPP3HTv7/CwLN1AN2AOixQNgxIJMGIpEkgKsz4dfzvBMC5sJG3P1hC4FtMu80/yt2VgCQkZSbQuzc0Ngh2n7/4OZPbwLKvhHCrbh64M7cq4WXCJgMRLIwHvH9lYILxba1+C31tYxmoObiB0VI6ABJu22r/1R4NeYPo3zR1dL1joo53eso2HRWRqoLWfmWkv7ePZoeVKcx3MlRk5rRjuqLVVuGdD572TfTsDQ00IAWlKYInzIbU4VoaFysAyF/KLjLMK/uAMKHXoa5mASE6wuDvdOX/n4LMAw+DWDA200ASK5zPobxLgLI/DLIIwZuMrgpANhtC14IA0AbzBlRERgc6IQRrdw/rh1WnPRIANvXKOpwSlD8bRMEgNT7Qlo81htIAqaDBTEHzYpG2fKtzZ9uqp7WYNZ2E1gKrgowiAzIzlko9iOjuTl7h9DdHYoosrR6k2v+/H2qke0e9qHCi5lLDPNm4XTikerLtOu0V+/RAb30YbfABS6jorNpiQfci+N1fV/UHWIMKAHN16vzR1XWJqT5uQo0ykg38GlGuY5ZYb9/Bks9p0v11JJaRr34tObdaGjc9X0eO8pr5Oc9X7j+n/8pstn0EPt3/y40fm6K529qTSYNzZn0DoywpE48TF1eevDE+//tckcnfXTl6ketkRUTyHVsFE4i7F1NkdWMQTQ0EEaDkEoDRDs6mrB7E3Gj9a507KYB2wmNFfMIWQ4IhSd+Eq0ZYqtQTpvvObjzwc1cDIQAECCBXTnfgQSps9aTTKEEDJ/rSL+1rpNaxTgWYP9W+rpLlJHWedJVDys7serTcAnKxTIsalm6GM16neZLv6gqLA177c7YHzNGjSaWimZpomFpaOBTjPAM6N7p2WLhktHQ+aWe00O5EdHQ+HgolK0zOf4qgLDfO59E7CLjD0dD44pylDJuYgfEgJeVMjXaCWR+wQ3du9kEZN78Shi4KWkMbVZYQR1pBmFEnxUn+joz6R0ASCGOy2/w4H90eSeF2Ao3h8iHGSCFDY1wQSFyI5MLNBTEMg1o2cDaGjYyyCRisTS20791UDW1EEm1jyCDrMbQ9UCHfDUlawwABJAABGSUGZIqjaHUaafshqapMRCWmmpDyjCerjh1orRJv/Qw9Ul6r8P3iqHSw2hZe8Oenub3OqpDCZ0Gj/354ip98LrbtBpKQwOfNshpFSqdIF8pGH/50vQWEXT5OD74NX92CB8MSjEBJP/9lxXqhmjJv/5DSaP4+tfFVWLcDYA4OO9l/GFEX/56cEsA0jO+ty0ZAPjI5wpmIw83Y4iBv7aDJgOSm/jVyq5UW4HoHHm8PQ6pKAJhJmF5xGFEJgVTgW2fAEiYtOoZbJmyA0Skf6Np21BSEtQxBIrNZ/WdnOAuUAgypKFsgYcmoYAeqQqwrBBCvKS20TGodbV18IHCDTXl09D8tFfUbTfWE1FYKUo7GvYHdVyx4HFKvxB16YJKHRn6OVerQXsd/qmkr7mWfu1Ep4WpIjSUhgY+ZZCtC9HZaQyftDmGeqWIAQAAH1weEk0Cqakpvu3fsizLjlFx7v8NA8DydVhPsQGnvfMgmrA2+3PYe6wtBhzoMF19+9LrvFjEodM+Yn17AXzQxYx+N90ECBsQ+2BPrWWLKG0q4wkJZcIiefyzJCJx7MQ3pJ7VwtrUpY1czHImg/jrXBzbSSQLqkILQBrajaaT2QFaIKkRS76cb9IaTJj0HeYz6UzpbZ+Q9AzlChoBluXRetyqcbX9qX7V4uu//vPH1HHf22+9o3rK393b1vN1EbSaKfslxqgXrZe8r8uJX/04JVbRlOKad4stUQv22pRSjH5d1qqLIaQR09DApwrR0Pg5nD2zfG7594env4OzE6MIPfO1YFsv+AUAnZ32J//sWHCSA9BiwHZhMQGrGZsnvQ+/4ZU6Yh8Zc5J/9/fXYjcyANBhRvqk97E3vFNZAWgyIJlG2xFsXgMAQ9ZrcKD/6M7Ce5J0J2D/t4MRbq4libxUiALXvsrTX9r1b+4fTC3MlQ0jqD3sXbsuddm4hDsY2ayZerTGLoFa3Mw6RUixnI6686NJR51CojPyowBloxn0cF7101XEW+3QmWdbHbW6cNuwNDTwaYLCuhC8xPNBF8O4gitx8PCwLBsYwVokOD/EsizrPZ5KNw8GWJb10Og/AlBjbGAkmUDbZUljaLbAeu3SP2zhxi46+q0WWBMJxLmcxkDRg0kzCGBzZeQpChCFa//YoAm3Ft4zmgGAbEWzgMilORRoDFAJXfVZseSjJnRoDKh5Gzl6pEh448dWYyiaPVGQfVCwHatvlRisva6+kZUaxjVPq3p40/OYaiKlTSCa5pC6P7Ke6cU4UT9RaVNK7X4K/T8S9avW/LEpUcB8CQr60VAaGvgUQdIZAMf9fYubdEAqIk3RgwszYcD2OcpkGnjCASC6GDNDSMfEeSskLPx82Pb4kxSOnqDEiztxxMCvxGIQsL4QiyNmJmEeYr3HSYLEoIedcP/hsTgAkE0HHxi2txogAFiY/BVpaGnHZgIAMttFvAlZELJJnfzCcZV5XWdfSm3KNcyV8d61WqnJZaWqBAGNQtCaINs1wkbv/fN2jaE6ly7ugC97pcROXd0tEXqcBXq41byrc5h+VK39FKDEU+t8mZrDNH0fVaSblpDN6l9FFbGNpX9acgCEWp8rNqU0GkpDA58eRGenResCw0wuIg658kLPAM3NhPNHHsrGGTjcgc9eRxxzPsb1fR5XL8MZCDgpmAFDcyu6crPWM0hcfvnq+q6QwZyPYRjfVUAAMltBbzCylfV1bGxnMnKipCJokZQ3ZwUnguwhyfzPK6rSyxW1hiqAqa6V3XTtJFpbk2ZZqQoglNG6cshsFHh7AAjvf68wZ1V4XW8g5B0IIj88sCJ8rD3V+5lyUtFCVcjm25I7ox8NpaGBTw2is9NwZq0L9CmlnvDgEM2dV3SqsI1OPJoLTrQNPQzKGWBZzxBMa6t80OUK8sA9fWZj37FVHKMpABZ6jAZ96uGp770vALDQAZYNDBoBwNzaDRgtdtgH7GgBIGjKuYwgiTK1bCsF/YcEIm/4TkmNgwAAUqtLhfaSMjGiGW3FWKplJyRAn+pSXyw7q9jF+uzKNZqm9whKrupVu0knaqw2WBdUZ3X/pKLub6OhNDTwaUF0drpXal9oG51wPz2k1BocbnZi1Gbtt9n6pXD8rqOw9VvFDM3l06xY3mkToMZYlg2MdKKpJdnbu2k/hqtvPeh1Uh0bF2bBsWI+JonjQxE/45pLAWhONG1ZgXgEkfmIKnIhHxUGLugbkTdSyP+nDJJQj83k1aRSLUk0o/+IdMnY2iyd3XewqWnBVXwiq9jBBLwRKmyLUeTZzY9S2eVUoaPFZ8mgKs6e0JObUKLUklpO6/Gv64E8V5maWJr/Yn5x/Z4RteZRkBhZNTR5Kx1UoYdh/f6gYk9RkPZZIkyh4Ja6+KMmfTXneiItStAp/ZMrjYbS0MCnBNHZ6d4hpTRwFFgXiiCv8VWbJzTxhHjdtLUYOfzbvZzd4/nclDfIR+ZvJDD4ZSdFe7wDJOZ8M/c7xW6JO4jHY0gBsB7rhIkETAZlf2lzGT+BNLSW+AWUP5Rr9OIG0GIxl3Al72AhG9OQ2topLdCUyor2WuUQ15pFajxZ4jVeWzHQtyyv7Z4ocQrXFCHy+AJJUCykoPQRv/TuXzZAr9jSelapVIapSamZqf3sW9ZPXxaa76TY+yyhLuhUqooxoP6dlIZ6mBgJUUXJkBJRLyVeZiPlsoEGKoaYuDkxakPYz8zcRXNv9gTO4px0Tbr/peGfTPwIT/7ZseClnrHe4N9x1pjdZj+02ESTLwAAIABJREFUvvJOx9cn+r/z7WXMcXweWaMFqU0YBIMBGYEkiUw6AUOr0WzZja9UG70gJ0QSNVjkCQAwmSBkkFLGVZAwmNCUH8spazglel9XB+3czvqji2W/LX0slnJZcPTXVBrUg0ujrMwovZYeYVNC89BJpPR0JfbaO1C1qC4goqZQEeW6v7oqlAb9bFS6nOY32+hy2UADlSI6O83zvIsJiv+0XqePL83GhvMH3XzrJ0bjGh+cjACY5AFrbPBYq6gneM/1e4eXvnPguOmni0lTe/dWbAUAUnEASCOdBiB1pjoy/MjiG9MEIBA40Ga4Fa9QFstitpIaD0rcfYj48IYAILkDk5io0NqL9BKSQAZphbdFpCmSlbkkSKlHJkmIBgYCzcLBNG4Wlf9FdQP9GoMByOh5QKID/8thgruaP1LHOrflqKUztP62oGoHyh2IO/k914J6PVTDPdFAAxVCCqj00KCcgQBtiW3eP9T7kws/5K8vA0A0NO5bWI9NTUX4FdidYzTEAskx49zV4YB3wGQaeKot6A3y/GtXkpk0mnbanIGxVoCwWAEUZBAuXr7rxA1BFMiSxtCixdQ9Xjpbh9nQUSIJkQSAI49oRDgSOHqv/A+LAQBhASBqDOK8ZBIggK2lgowFErCgTXMzkbtqZ10SgoE0bO0WZ7Ae1oSMSd9xyPiUB1dVW+k681R5r1UeKg02rPpugTzTv67sMSm9oh65onaol3U9lLWcVyrPij1IMTolHlzPlFrGyIxphnroZFX/0uogidKeiyo0iYbS0EADlUEKqBTLUV+6tNRK8b/C0L1XFE0jI/OpAyMjFD3mNAYnObQ5Ayz7uCWFVKvN2m+ztf9sAQb7GBsYOUimkbafGbU92EdCSFi9zg5jsrkDsNqzgv+Di5eRMRhgMgOtLQYD5FBKAh2HAPtglwHAB14ugQMtMJBIrycBwAAjAIuZBIhmNBOi3M8AwLXXJWabH3FSkAoVCHjvfQCACZSTJgEIisrOrb0woS3fQGEwA80nBi0wdiCOTV3SnkB6K52u10HO/OShHOEchCTUfTM0kJz0ceqrPewrxZuaFnP3ltia1Xt66SiEYjKm7FqaV0oIqmJENGep3eH6neiayxXQLHhqPTwUC+rUJFJaqJdgXj29xPsv/QilpxejUwD5F6iHDT3QP1JEQ2looIGKIAZUSs2uDv/0p6k/epzmzi/32Xdkm/xWM+KxqSmem3ynl+qmkPyRyx/+ixMmxCYZV5DnuasW+v954QnEriUEIxILPoZxLQDNXZe9wfUUdtaBWCQJoK8D6KcHgVQKyQQONCXSCu+EgPUbQGRuVbxmbjPd2kY6g8NiF6s0UgDiiQwBYQc7Qt4RvkUMxdx5Pchjs6DtRBJ88EeFeRO3lpAU0yJyZox0Ati5PBdHQtl08shYf4ljvuiQLfuWdSLxoxt5hCsG1a2+tjxezNBQdT0c9SZeNMqsZie95mdN+nrc52q2a4xhrEieVcRtLbzptCtUKl/3FHsaNVLiSRtKQwMNVATb6ITbIZWWtLel1iLBSQ588JUI0H6XDcDPX4/tWgdpyhkION/nrhPD3ic7gHf9Mya0d6L/GAxAnHMxDOOdT2ZSMNMBlvUeJ7ATG/SIVSTsaDYDwOI6sHD1gYCHxok+EzIZY+eI12kFup1e2gL7oB2WrgF7N4UWAxI9XxyEqXNkrIOEWCex5SAsAAmQJFoN6GgjQJgOHoTFesKUBppgxuCJFqTjBTYCyjkmlmnsGsuWmmh9POC0Sz4KeVzLo2P9MB4yAMSBA52Q8ix21j9jBgCQPRYA6FCnfXQ7vZJ5w/znAzqbYJVGIQnNVBOj+hK/or5290RxQ8N+o3SIpSb2TrYVE+F3jhwVUTbeE1o874WGVBdU93orZV6P2iSioTQ00EDFkHSG6OxSWx9oSdJ3I/bLMID1NaTJwaHe6VmMepyHWwEA13/JdR9GE3C9mb7XiK5TknbQ2Yl7r7gYxnslDVj6xJTQd65jt90OgD7VD6SWv+3jcHkxia1Uc8dB6zZg2gq+9EbcGMERa/zm5cj16yBJWJbfvLdPWJuavJoABDQ1w0Tinm4QQCaDrTS2DU0QkjdvIh67vAGkM0hg7rIiktEo7TB8cFIMZFidlEpNYOuSKxgpDDfYfm1ywZhaSwPCre11SDmXsVek7huZ5TgArKvdFqshr1SHMfW9+WQNdZbkLbGQREZrs9RwWbS2q1WW1QojGvYMVWgMtwvFHAp7vWgVU3RqVFVblfSgTE6j6m5pttXjq2Ne58tpKA0NNFApsuWoXUE+tohsiag2WBdmwogmYDJvPu/j+KCL8S3kBNUKj7U1xBa4ayms/o+XowCwHV9D9GiAZQOPtYBIvuIPIxr6xtwOUvjfAk5q6aMVI2LcHEANWo1A+hYfdF3cQDKOSBwpzM3EkEpDiCNOIL7FT621tnceEM/5iR1srOHqSi6RYWcjA7Hqw6FHP2MAgNZ7LABhOCSdy1NCwZFdM2s//5+pbLqEoBDchHpq3r/TW9lPmnEQRXc5VVEoQeuifKcARx5Su012NBSJAz3FDA11lIgl4gz2FPuwVh1NHWW5rUgJuF1mg4KlS0v00nfVL6Q6/aCWt9FQGhpooEJI2RMBJ0V7WNaTKyx5uJ87//KlN9HW9cWA2B2zZX5l/aY0rb8fLQZQYyzroWGecjGMK7gqtGDoD7MyygS863dNDw+agdikK8jz3EqqA7QnMLI+F0sBJnSOeGkDzNm4AgvtHemEhX5uYBfNu8DaxsbarRQBGCCqDiBAKDOrCSItoOuB67/OAMDWB3EA6RsEQWTNDOKmZJRKLcvxC8bPe/tIDflsJnOr5N0UQKCFhHy16LYub1xkK6ScDiMMpIbmQBBSBkb+Xqe7UtS1N9XpqpktNWc3/vY/FidSX4moDgPcB1S91n4qN8qF9HBbesCemg1Ko+5LV/H1FaxbI0sNpaGBTy3CfqZoyBsAREPjjKo4YDT0VdcP2s6M2sTgBrGwpKQ1HP4ie/bgTGRtLRJ0MYyPw8JN2J583Arg8G/TC+vHjU048KtnGN+/9sVoD8sGnPYDZuBdP8O4frwNIQ7upz2BicfbDDBl8yexDs7nmtoytLRj8L7Wtf/uvX4IiV3Yu0F7PA9zL72bAXBXexN2dmHus0OUommI+RGkAEGA2QBLB8QmjwSwOnvZ1IZDRnT121sIAYaMICCl3IWy9oMUJI0j9a/exQwyQlb6Z7eaREZR8yHfWSBgO4M8bYEkNCrDyNEHmS1I0ZdJpDMaWoa0TxLGegoso0b4w0m6fvRrwW0R0qUhs3HbPf2lUTt7tbz8vbZqlKWvmXNRLzSUhgb0IOxnmNISdh9J1UghO13MmZytlMzNTZA3fQoGxHLUYsfM6Ox0m3MsJ3Ni/VK3C9znHjNeuZnEZW4FWF8Ed/5VAEhtgfvlEMuy3gETDKC6cwwN0hRFD8IKK4CddKLtuT88vGXawc2DsDQhsgLO51uiD125iTjn5RIwGGG4tvnYCKXkdpcACBiBVBybAMgDggHIuSGMBgOQaWpph90Co6HwfC8ASGtFFQp5n0lAUOxUBeNbs0UhRG0GyDNKVJx+KejKptQH40GL2qRBbmhETN4m7JsFQqecu41H9v2B8j1U9/LLvqKy8Zgl7qrp77+fq6E0NFAe0dD5JWfA0xs8JwrKsJ9hGEY8hYf92uJbGpMdVoxU7cwULseorQOa07/+wtWe00P5WoNMJfdMM34l1bDfNbWGjWsYPNUbPBd61c8wDOPjwAd9y2dYt/3b3792/Z1fLQHo74e1D5aFfwwDy9cx/11mkgfE9hMWOnDKyv+Xb377B5H4DrDg84dh7e/uPAB85sHpc5faD8Mmxk/2fJl9ho51dgIQ3n/exyEJLPM5hnnuMgBYDoIESKQtTYv/g4f9UGcnqG5YxK2jCdsZEJ+5RxCA9E0cePTRdmTS9hsprC7wt1KAkNneQCSOVDqnDIg7k4kASUiBB1Zz4btszQ5tUkjZZiBj7rcacprBVn76JgEY2rQO+AS0nBLQ3XWjCiGWurmmKndtOuUdqJjQxxsfd1WgrLqjU5ruxXvQXLq0OlKpslLFeM3POtFQGhrQgZNnJ0ZtDjd79iSyYlfy5YdnloZPquPGpDEsy7Ie+JRqRR6p2pkRFxtnfPCwIgLOHh3TTzvb2h46aXMM9QYvhGUyWSo5lYRb6gmwLMt6xOZWDnfAacegh/3G02728Xcml5xeJ0WNjFCwcjPh6IUFoSU+hzMs6zl8uB8rffdY5r4TiiCNjbUBOz3mpICra3h4yPb0M3T83bkk7F2wAJyPcQVXUh12fuOzjxNTf/+hgl/HaXpnTfkAwibictqj1TlCobXbCCCTQBvVspjCCVN8bQ38CuIAQIJAQkDinZVdAISh8/fv/tcNZJKR1j5rruZCM1oJgITJgnYSyJoSkormUjG5ehUhyXYpnFFASnH43wGQvob7jhiKbYlAOp7SCIEUkNEu46CzRmS5/Y/Up3wkX3n2u/oW/KRi/2MGa1lRZ8WL21Vl4TYqZCWUg1reRqP3RAPlEA2dcwXF0y3tYd02pYUgPLM0fFoj1tzW08v7LoRH3Q443KzcW7KQVO3MANHQuWCvh5XbGdtGc1n2YT8zM8QqOh0rplvogA2wDdG+mbDb4UB0dpqnz4hDRZ6jIYCW3Av2HopfjgC22MIK5n0MA8AK6vEvWzGNg4+f6Z/yXUfk8KH+jq32+5e/ygRFd0UMQNAXMaPr7t+scPNSswrOx3AACSKD69ty9YBknI+A9/3XNqS3wW+DX+EBucEFAJghRSoYYD3SElvcBmLBKQBYBSBYu1uIm6YULi8qizxnCLOlOR7fITKCAAjptanvAUAGW4sxAAeMuJUCdrAFYBfJeEGFaC0Ikn+j6JaTvhm7crP6DVJASxu2d+rd8yqjU/k44n26rut+fKHuwiAoikbXSwCXsLd/HCEUbwxWOyrNwlXHoNT+ehuWhgZKI+x3TQ8HpDP8ks8fhm30TG/Qxfg4egjadgYgurwEcL780AM1qdqZCftdQR6cL+ecUK7ocCs1htz0gNMu1VdifJycMwmqx16WAS/fJlZmCNCb4IMuV5Dngy7fAiyHV2eWHjpiBLpO/gFlH7BTzjEa/UeAQ31NSO9s2Jxj/Vk6zbD3mSBksLOFeP4SG5swm3GoSzT+02POu1qymQmf8XgHTDCb0GRJJ9SyPbYSmb+ZBECaWxT+AiEeT8h7hflwwaRbKcCI/n50kiCK5YFrXSwY23WP4hxPtKoHlICafmoThKbGsAe7saou1NAdU9ppb6BZBkD/lNt1ZN8H1CsmYI/eTxV1O0qXcKgODaWhgdJwuNlsgTxbT698jWVZ1l1UZwhfkGS7pzfokoMMtEjVyozDLfaNYmUoy/kV5Edkp0dnp430oFSVSZEzyS9HyjIQeLJD/Gi7qw3WU7l1zw/9amnYfi0KzucK8pH59d6TTwz1L1wz4zN3H8DGNfDByc0xNuC0g0AS995tAmlGK9Dc1yVGCtKeQL8RpkHvv9nFjRuwdQCGub8LfrQtgCQBIw4D6xkkkkjGV9/PHpupkc8pNISjVgLIJLZTMAAgYD3FPm5RPEDiOgCLEVDGPKawsFBKHhcEOuR/AoAO5+c3P1Cc45vIR4+36LdiFmx+BJAupnPswVasqgv1ShXq7McGJao6FhN1t8XArt9hUd9IwD1VifZZ09qjL4sQPpEaYwN1RdjPZBv70ApHQNg/vjw0PO0L8qCcgYmTs+Mu6WPPBeZ8T0AS39HQuGv5jDQtn9TQDKPVMSgPyhV1UKCcAVltCPsVbOibXowB+SGioWddwVj2ntUZeHHUhrCfOY9BYIufXkAaJhTY+I/0gxwIPD7tmuRV5MuBeuTepddXmpAx4OEh69z/jLXb0LRhvrmRKLC2W2jvn7ztDa4UXHyu45+ejwpoAjK5/tRZkOgUSzY2w5xEQoBAoBXYkscYSvoITEAKBqHokFIujLpgbxawnmJfFD0UDz30kHz1zTffrP9SlUDTwlyp2VnPabWA5j4Uptz/FeuOunw7OpfQSVNPOe0q0IhpaKA0wn4xyNAtSX/FnZml4aGeaTgDAZw7NxtaDvZ62LPL4+dmoxNuzwzjYqadgYlRm62nF8vapBxulnXXxIzDzXpQELiQG7vkDCgDJ8TpAefShZ6zPedzDMjKhaxRUM7AxCiU2k6WgivYZgVitCfQc9413xYUgw4o51jv9K+u8zFz51ZHCqtNnXffNPHW2P/f3vnHtnGed/z76vhLJGWJtiS6FWWLquWmqeKkSiM1ctzksBZGVIRRMdlI3YVegdkWMwOy0DUdSgMjhigokna2sGxUnCJDlNULbG11rps9FxmY1YlSKanWpYqXWqqpSGQcWrYl2aR+kafbH3f8KZKmftmS/XxgGOTxvffeO1L3fu95nvd5TCH9GJvERH+Vrt/RAQDQAHNARA9MqtSIhIHsc5/3nU8BzEGrxuRoYH0erg0jGtsAoKoS/gA3GQKCHldn8q6cBkHPS7L7I6+qVjPQOyVBU4DZG2BQ5yFcvAGz16DhMDODaQYJgITJxC4iWQc3qzRJgsGyHqPXMRNe7IQeOyIHrYjU0lmJZAmEX7ScYLteSRvTsOy+/AX1nNbxv0LRALd+kl4TsiALmb6I5T2vlQgoWQTkniCy4h8ZAl8fnzaHRqLW/p5uT0XmGIC6VkFw7zjnsNls0Sk9c1dLG0zdbvtQwgINf0+PH+g50jZkTzEyKLsrSZkSBhDzeih+F8XHUdbUHlMM8mv/yBD477wiCMr2UI3iF2kv/4Onov4v7eaSRxtRpBGLHzAgoAuYrUX5phu6wglcB3inmzeCm5UzH0xCA4MR1g0wAhKgXYfKAtlNwdQqoOhzAJgOvNNVowYnQZxD6LKlYl3KNRm4iMmQCIgwPSU4eeRzgEZ5GhBnUdncbAEYEB7olatMzOq/YeEgISwCgcsYi2BGBABJgpoBDBuNUCU+omS+O+nlZw4OXNIOCN/ATF7WXAcsH1n8IbEjishbnIU17V7a3FZPSCdv4p5YtNU3rcn9Fhv/F2HAX97V/7cxnfMqHMZCWZD3ZIWEBYkGIitlTc/ah5QwwxdGKqwxt39Pt4evryt7eAc6HY5O7Hi4abd9qM3m6EQszKGsqV0QnHw00DBjV0sdTFlTu7BnxKEEQjqOj8j6oKI87qVo6fKv4AASrkb41Cmfb7a0eKjfqJlGoE+9rRjTM3qENjcOtTk8QWX5oAhwhuKJMMDBANRW62eu4+KNGQBVxVI4AoxfAjT5+QAwPgemhjgJ72Vf31XozNF0y4kwVAVa2ga+IImAOv74f7GjY1QFCVBXH6zRgANUV9/yyYPgYFqXlAMhLAGbnM1bVZHog77cc9KdNeFN/uf1YDAwiIkZmiQEZtMUdUhOMj2FhDSS6miLzUXRBpVP1WrAOExluu9Zt8o5qjVplYCUTurM5LZ6gjXMs1othEyz0erJhbAIt/1yefpzvAgrnaRo9XwXy0jaH978L24ZvkSKaSBWgIRognSBBStPPJDC39XiOLfjFg5A9ou4dpx7Dc+2N6Gr5YWREi++KbTW4fQh26tFri5XTXQx6NUWWwdrFo42+F/d6/jVlMb8513tG47YfuKB4Snn/W/84/sYUxZX6CstkxeTRY621n3yMLpaHP9+FWPXIQHQACbwX6/1nOyV25hLEboMVQHGbwAApwK3aWtZ8MLkdYTKzcGBALLAAAkMkLTgZlKzJajMKN/pqjjh8kwDwHqjaiwYAYOkwiYDhsezXqPEzNNLuQFxMBYgmHIsBtM6jE3Mb52vxVSqs0MHbjr51NiDwpt/o7xOjGn47W/jMQ2Z7ppZvM6ZPsrdUZ2713w1GLHTskqCFZY+jPlXeBHXfBm/puxntMyhFSQaiBUhHnBoTXUT3Br8XS1KSobUUMoFEU314O9qeUERASmBDgkxki2OzlHgIafQWtdzxHa83LXjnKvPAAAlTzrR9tonep8vD7MPOZ1oO17ubm9C1zOON3xK2CSXksbI2tiIUxN73KGfOHrz7e7Opv9tsXUYnMLzdUDfoW+7vGLM9Q/RCKsWo1eTF3DOi8hM6Ptrn576TZpwAZUOkUIDAqHoBh0wl9qNToPpMGAoQokGV0ZxI+phz8uDWLAZoU+4MDitem4mEtFLxikEF3SXSa8k0m1lUK+rmJsYSp71YSrC2FhqW2zaWTR8djy5JbRg0yn9PigIWUVDjpEHyE00YNnv6atjYk7LKhnbEoexLGexvNP84n54i4PcE8TKEAsPEG6HYoDiHJGXhi7B2uwfGZKzN/hGvBUZSybLBPr9QFCJuqhrFdqbzAD0Jdb+kH13XV2rq9Q3idkg4GlrR1RIqVFWCr650RRVDFa7u9kKmKvgPXXKC0+bozeCDd9sKgM2mtT4v7bnegD/78bERNc/EIR3Bg8paTEPfp5BVy84tysNzNGWmm1KaQrvqd/MaPQmU8w3ACMKjQDytjVXB0IMiHonphMVAyd3NT0LSEBwHN7LimLQV5okCSKA8U8QhsggzoQ3fUWPSczMv8voGmujRR+KdiWtCQWQUBoDYOUWbDUqW+chIRIampu3MY1iADB8NjivJdbNS1zJppGWJZroVyg93yohi1MmcftK+x1yZJUMIxNL956sqM+FLA0EkYq/q+UF7L3/l65TKZOP1d5c0tnRWy0/7cdbO0b2CK2WV/c6knfgm+1DHSN74gs7K5vdj5x1jOxxoq37i/ahjs4S3to7FELFvuiK1OdsbbJjocop/NQi91z8M9vJAAArz4c97/gQBmDla72eXgAwIjU9VAqmDRi7CgBQg39W2D3yzA9PXLoxnVxR2rzzvmtnf58xPQIw3xCiQe32b3zy67cCIgCYdx2cPPnSjawjuSkqllv9KhU0kSTjB+Mg5Zjv8eZom4WTDfLLRSy5vO2ugVv/NL90z8vaIsfzyv5LWHbbwC37FsjSQBBpMe98wmrla1HLW2FtbLRa7W7hWZztrTQjNauiTNnOJ6zgnYLg5JW8Ua33y5/UtQqC4LZbsXmDsmE3/37H2a8KwuHWdvcOQ7SHniNto3Y5Y6V1IJqm8sKRkyHUOgVB2AOPD1r5EBWe8wDvFNz2EuVgrhoNUO1MtDQIgps3IrzNZbeiuhqFKrnIhe96gmLgYLE387h3bCwMSEC1s9madFoqDsYCIKoYdPVuuxVGBhXQ63krIAJGVFfj3q3FcultXdyukVppSqMCkldmpJBrxcsIlrHSZSqssmEpu99Gs0HaODhiiWRPoJlLdIv8+rYYXVbi90CigSBSKSuvqCgvKyuvKMGo9Z7de3jvqfe8FeVlPSc60bhNqe0YK4npeEfJbBDo9+P9l2xH0OrEa65nMlbbDHW3ONDivPeDLr+y11B3tFwm05cBKHu4wuh56aCj0wvPSY9GBwPg7zruqZRFh//ITz0Iorp8pMXR6YXnxeeUtaOF19uOdHW12F76VMKMzw+gvAQP1ZtHRtHfjwl5zSWqak3goNYZDQBE+Do7PPB8GlZBrUflaNsvLqFQDwBqA6yl0M1hZvN3S6M3n+m+v+v0IighEnvSD+JjP4Ar1yQAmEY0uJJTdAYX3Xc2gqgwMOgAMIBBpdPHtQVLap8F+eanj6U2XpyZIf2BVsFEu4gFgWnD5oklsogEmrn3k6mT7Hoi02/jlvlcSDQQxDzqWlvr/F3HPb0er7fT0eYBAvC02drerdm7U0mUICeCEARBaEYAQ1eVPaeCCME/MuD7KIBQcqcjo3KGafR6vHx9Xd3uHedeePXVFteQBO9H5W5BcPLAxQ5ZiHiCmLj2J41WmNTAFQxdBa6P41MAwNUT700DQH+nUntL1es40mMu1GBmBp7Ocztc38gH8oYdimrxHPZEHRg6mHXw/2EMGi48zdbJZS35nVZgLBABy4Nei4Bu08QcAIRD8F2GyEHv+/nl2F1oZsBihkkVtyKoAHEMoyhOTSERncjFNHew0DQACZCA8KQIlMjmFgng0raPoklKRzcpQZLS2S7SzbZMPW+zBBgrUhPcSdm9PVlZltX/a25BYC6JnOXERGsxNcJtIZOeyP7buDVWLhINBJFEn6upydUHlDW1u+1WuayFsxZGAPwPXOb3/nMUAyP+nm6PaaTT9TqA+zcbMf6ZHxgYnIUoR+WpoZ3Db/9BKaYlF9byTEGrVsIOPW0229+WP7vjg/8eBwDjTKfDZmvzADolBxQABP/rw0moOag08HbYHKfGEB7HHELvvp8SqjcJ40cjH03MQAwA8Ha63poCVJusQyPQM3i6r2qMKNVBo8Y0AtOYnACmRODGJYgQg/Cc9UIfMjLMBtE/DIwPxxJORjhAwkTCkgNmgAYIRuLxDdFAiN5YK21tgw5g0fhGjkuXWCK2uwgAozGFJWYtSjUbSfmUU6VzakhpOpHSZqg0fJpgpJBvw4sVDWtusl9Gsjw0y/+W9+Ksfv1xU2vB/C0rcUYrYX4g0UAQGfC/d07OVOUfGbVstSjLIgx6q6/zn7qHoo26XvCoTYbYU3YeJt90dHpxXYR4AwB4pxNtz5wZgCkPMwhDg6pqAGCXXjvxsW9sDNfCCBW6BcFtLwWm4WlzeAAtYNzpfkQPNofJWQAWni+EKELEpBSs3gQw6Hm3k4cRsFTh8rlfD4URBmBqdO/SAZgZ9nq72v7Fh6pqzAZxeRqzciAAA3QwaxVTgTJlTqLksW3QwMgxRN0FuvqDn5vDlAgwFEVPUApi89drwyk3NxXw5unYu5ne0xEGKShnmAREID9JNhirCmKvY84IucIWAEOW5PZsfn2pdM24eDNtNGEU06bGWICB/4tdCUeTb6vTC81Ump5bPLGtnhUB8098eS9C9p6X97LPXwmZe8+JQir7N7KicnPZzQ+0eoIgkuhzNbmrXvD7AAAM5UlEQVS/0PXK08CPd9u65UdudVHjt0ynJvYIrZaulhPl7cl1rqqqEalxtze982e2n9/Q4bHvu8uPO97wKuH9unrh+1rbaxPrfH3X2bz6AJUbMHxVyd/IiszSeNZsS1H0mzA5rLxmakipYYFqIKykNWD5cvpFAJwKonykhJoOsewHqmJErijdF7BJJfVCzMPPtJCyVYGIocK2Gv3ve6ekUgMuBwGAcVCLkLCS0YupLCVplKpZ+LfFrJ7IMcVhLtz2JRiLZn5oRabLsuhTW+K6g8Vd22XMBzW/hwV1ftt/G2RpIIgkalxdrzwN4PV93YVy5WtXDRs/90f5U7lwRV2rsxpg2LBLENz8pX5jxcNlQFUxF62VcAWzgKZeEFw1c937Pii3qqsfXZf4Z87AAZVPNY5dRYRh265aALJiMO9y8xqAAWZ78ya59Ua721kbs7gX5U8OG79SpbyTwiiAxlRbAkC/GTG3fx4slTpAmtpQvw0MKkCEuUAubCGyCAdAh+h/QEwxAJi8kYf1ahgMCRNvomJg+ZV6cIAa1sbGDWoGABqL8kQfwYe9UxIkXA5CBWj0kOZQWpSkGJhGh6xoEl7HbRQsU+hiKupCfdrt8t75/HfXM+U97zyYYGmQX2oWuXpiGW/id0DmhtXJbXQhLZcp6Lb/NsjSQBBpeX2f7c0yV5erBkBKfW8APUdsL76N2Xl/PEoyAwaW1pWpw2PfF+q7bS+8jTkVxLCliPkm8iB9qdke6ng7gOHJeMsvb0F/f2oPFjPCBog+XFHDGMIUIAL6SksBfNe8YBtQWmAMeINhQF3temTI9f76DcGZL1VeeediohmfA+ZQ+yP3Z887hqOD5IpQGMK1edYAtQGqSUxHqzkwQGIwP7wt8N6HMU3BqSGWN9T4Tp+XMB2GkjdBtV4buabb1cxOdsSDItY3FF87PVakFccz2C0YzE3ue8843g7GJQtTQ4pEB562rkRyDyh5QH35d9FT0TFMp9+Fgf+R++PnHZeSPtY6hZPK95xoaSCIu4201jWyNBBEWp5+xVXd75JXVTa5Ak3tQuuFfUpRLNu+C60Hi6MtS62w2t2CcxsUD3vtJlWSYuBQVImt6xlUUad6WIIYBuAblwAO/JP3j4xiOI9T5cNqf2IdwHEoedLNG5GvBlfpEgThES0A+AIIXMSVWTCGEvvBL6rA6bC+SOlz9gp83mAYYGo88mQsX4Jhcz4AcPcIbrvFpAYYp/mK65vdjmFt/NldHMc14zcarSjkmNqIWt4KIJ8hHMKUBGxy2a2oNEMDMB0Cf3xgZ2n8BMUwcNEzXopiLXRmGGVLQeTaDDBxsmMc0MUe56+dvgJgmpOPy/Tgd25MeuKTEDjp8ASTlIEUBiSwPIjzFQODikNSFEQeNJbykninyYqBwci7Gk1gUDwY5amRDiXxxF0fJACCIEg0EERGalxdclbmXaa+5119wNOvRFNjv/I0ijeqYd4lCILzy95SdDpsbR8CUN/nFoTvFQJqA6qrlY6+7uz8Dn/hWsLcZWTgdOCd7vvU0H7ZLhfARFCUdBu9I4Z8BlFt8bQ5RtdjKoI5AOj5eAbgwDsFJw++2V4ShLfzpfMRzGktvrFZAPkMmmJYaizW+4pihxKD14HQxCxUgPixzdHpGwsDEXG273j3ECwq4MtOOQcUA9Tht055MSFKkRkY6tsFwfUlNcy7XDUqSMN/98F1XAxgBpCmUF3SeTYAwNpot5hMMAKVO++76IMviOkAgvMKSk5H4q95p/PB6Un51pNn4j29RiMDi87cWqOcrKq5MPXrKHjsQZh10CW4LpgWpiLo5hBJUA1MrZkdEzPe2QyNJR7XqTFIkF0zZTXrY8qpgAOgqsi0K0EQJBoIIi2v77M1ufoyfdpzxOb+LP7WsMOt5GRcf08ZUPbMPapwCL4vCYKwywzNhBp1rQfXA+EQPG22Ng+CQNETQmudLz/Mpv+ns8DptlvAAHHsM3je0ashTWhcgvD805sgQbrostl+xoEBlY/W+UfOw9NxvNglCMJTJkBV7nTvMOg3b50CCnjBWR0e/X0QYXNxHQCEJ8Lmr3+3ujS2cEBjNKsBpjV/bfezVZeCQH+bzfYCKjQSuKq/ctutKAKTwoXllp4jtpcGw5jwA3lQ4fr5qwBQyAEalDwpp5v0nur0jY0Bhdj81e/ZrbAYoTODb7Zb+adqNAAUBWAFoMbWSg0AT9uLo7UbZSlUXN/qfGDwhgRJhMX+lBVAUE56saEGanDYyFcpMQ2W77Z+szYgYtpYszF63dUbLWOyEyJPTpS5FUBeYal1R/CqBBQ3262wlkINMA1458HPMyCCZwWhuRIMQKmzta6n73LMUmFSAzBYlvjTIYg7GYppIIj0vL5PLvkAwLxLeOXp5E97DsumBcSraPa5mlz+J+WW/h/9qaM/DACaGjkw4lfNT7507WHhxF8DwPG9tjfGAMBqd3/1rEM+jq5e+TReoBPmSlPg4phylGgRCjCjnIJo2+dVH47e4/7X58vkupdy4IK1eoN3VGUOBAIAV+n6xVFzV4vjnUlEpjEsF4zexNcOe3rllv1KXqpYRUze6fxjW1t0cYaCWo9HWoXWuj5Xk+ujOcxELQdWu2vHOde5ECr2ucuPO86FUPEo73nrtxbN9apv8+93eEqahfYGnG6xdQwrzhuTCQ884y78e8cvr0MEeKfrkx+7PimAmFzh2tosbP1nW7zGVLVTeN5yZI8jlqgKKtT+0H3PcccvRxBUEktvLGSfTUgAzCYuMJa6GtNSafRdNJgRCCjBJ9VO4fk6f0KnOhWm1zcLP1tSImmCuJMh0UAQq5hEIaLg7+ryNTXVyS9T63SnJd4sWuC7LM2+8WLm8WLisnzhnUKrJSZkrDwPD/YI9d1H0Npal2YYsZ4Sq6Inb0ysMJ40POVwSDOYlDM6tyPauX/e2FotaftPIuF8Y9ymMu4EsXYg0UAQq40+V5OrTyntEDVUxEmwQ9zOSS5Buyz4U4Ig1iokGgiCIAiCyAkKhCQIgiAIIieyZHknCIK4E1HCOdVGqEO4HsbcqqiHTRBrAXJPEARxN5GwKoYgiIVClgaCIO4WSDAQxBIhSwNBEHcZSe6J8TD5JggiZygQkiCIuxlSDASxAEg0EARxN3NrCyQTxBqHRANBEARBEDlBooEgCIIgiJwg0UAQxN1MHnkoCCJ3aPUEQRAEQRA5QZYGgiAIgiBygkQDQRAEQRA5QaKBIAiCIIicINFAEARBEEROkGggCIIgCCInSDQQBEEQdxCDZ84MruS+S+l/7UOigSAIgrhTGGzfXvXchZXbdyn93xGQaCAIgiAIIidINBAEQdyJDLZvZ9vbB9O9HWzfzuIcOJO0T+rmwfbt7EC78kFC2/RHjKMcTD7umfhH29sHE1umP3psa6azmNftgTOyGeBQN7oPVSXuc/Ohpt93/umktFnMFV7zkGggCIK4mxhs3151YveApDBwtL9BntUG27dXHao+Hd8cnxCPHTp/WJIkSXr58Yz9njkQ31saOFrffejF6FzZfeg5vBbfXsX2xt8ea5Cn1MSjx8eUlaRujzUcOLOl5d2Bo/WoPzogvduyZWFXYCBl33Sns5T+7xTdQKKBIAjibqN6a2zK29LyrqwFzrx4qHv/6agq2NJyeH/CtL+/MbNakBms+kGCptjyrd316L8QFR37Dytz7JZv7a5Pfdt/YTDl6FtaDu/HsVM3nWfT9JMr6a5AzqezDP2vWUg0EARB3E1saTm8/1jDfN/AhX4gup0xxhqOITZP1t9bddNut2wBcOaAsnfVoe74Zym7Z+gtcXPVvTlM0jmMKv1Q01+B5DZZTmc5+l+zkGggCIK4u3j8Zdlufjo6tUVntvqjMZu6TDYjfCpnDjDGGvqVLgaO1q/Q6JeDjFcgzpJOJ4f+1yokGgiCIO5O5KnttOwJ2LK1Gt0n/mPRGQjOnDqW6OsfOL+AR3OZxKMPnO9OtPCvFElXIImln072/tcsJBoIgiDuRJJUwGD73qiB/cyBeW6J+nurgMd/cLS++9DeaPDjmQPsZksQUkk42vaGYwsbrXx0JYZisP25Y0oYRaazyMSWrdXoPj+QtU2mK5Cyb9rTSWyz0Ct8R0CigSAI4o7k8ZcHjuJQleyTP3/49P7oZuk04rELVSd2D8jP01ta3o3vwBr6jw4sxDvx+Mun93dHj3Zi98Dp/TedvJPY0vLuwNH+BiWCoPp0NHQww1lkHkfjfhxryOoQyHgFEvbNeDqJ/S/0Ct8JMEmSbvcYCIIgCIJYA5ClgSAIgiCInCDRQBAEQeRGcqLDlHyJq4s1NNQ1BbknCIIgCILICbI0EARBEASREyQaCIIgCILICRINBEEQBEHkBIkGgiAIgiBygkQDQRAEQRA5QaKBIAiCIIic+H8xDvS2GuEYUgAAAABJRU5ErkJggg==" />

<!-- rnb-plot-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->

#Filter if parties are mentioned in 2021


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuc3RhdGUubmFtZVtncmVwIChcIlZWRFwiLCBcIlB2ZEFcIiwgXCJQVlZcIiwgXCJDREFcIiwgXCJTUFwiLCBcIkNVXCIsIFwiUHZkRFwiLCBzdGF0ZS5uYW1lKV1cblxuYGBgIn0= -->

```r
state.name[grep ("VVD", "PvdA", "PVV", "CDA", "SP", "CU", "PvdD", state.name)]
character(0)
#remove dollar signs

blm2020<-gsub("\\$", "", blm2020)
blm2021<-gsub("\\$","", blm2021)

Sentiment analysis

# create a list of tokens
#https://www.kaggle.com/rtatman/tokenization-tutorial

tokens_2020<-data.frame(text= blm2020) %>% unnest_tokens(word, text)
tokens_2020
tokens_2021<-data.frame(text=blm2021) %>% unnest_tokens(word, text)
tokens_2021

Now that we have a list of tokens, we need to compare them against a list of words with either positive or negative sentiment.

# get the sentiment from the first text: 
tokens_2020 %>%
  inner_join(get_sentiments("bing")) %>% # pull out only sentiment words
  count(sentiment) %>% # count the # of positive & negative words
  spread(sentiment, n, fill = 0) %>% # made data wide rather than narrow
  mutate(sentiment = positive - negative) # # of positive words - # of negative words
Joining, by = "word"
#do the same for 2021

tokens_2021 %>%
  inner_join(get_sentiments("bing")) %>% # pull out only sentiment words
  count(sentiment) %>% # count the # of positive & negative words
  spread(sentiment, n, fill = 0) %>% # made data wide rather than narrow
  mutate(sentiment = positive - negative) # # of positive words - # of negative words
Joining, by = "word"
blm2020$sentiment<-get_sentiment(blm2020$text)
Error in blm2020$text : $ operator is invalid for atomic vectors

Now we will perform an ANOVA with DV the sentiment of the text and control variable the source of the tweet

https://www.scribbr.com/statistics/anova-in-r/

#2020
one.way_2020 <- aov(sentiment ~ source_tweet , data = blm2020)
Error in model.frame.default(formula = sentiment ~ source_tweet, data = blm2020,  : 
  'data' must be a data.frame, environment, or list
#2021

one.way_2021 <- aov(sentiment ~ source_tweet, data = blm2021)
Error in model.frame.default(formula = sentiment ~ source_tweet, data = blm2021,  : 
  'data' must be a data.frame, environment, or list

#How can we measure how many tweets are made by location?

references: 1. https://towardsdatascience.com/a-guide-to-mining-and-analysing-tweets-with-r-2f56818fdd16 2. https://ourcodingclub.github.io/tutorials/time/ 3. https://cran.r-project.org/web/packages/stopwords/stopwords.pdf 4. https://www.dummies.com/programming/r/how-to-search-for-individual-words-in-r/ 5. https://r4ds.had.co.nz/data-visualisation.html 6. Anova and sentiment analysis: https://www.kaggle.com/rtatman/tutorial-sentiment-analysis-in-r

```r
sum(\\(BLM2020))
sum(is.na(BLM2020$Datetime))
sum(is.na(BLM2020$Text))
sum(is.na(BLM2020$Tweet.Id))
sum(is.na(BLM2020$Username))
sum(is.na(BLM2020$Location))
sum(is.na(BLM2020$Location))
summary(BLM2020)
summary(BLM2020$Location)

<!-- rnb-source-end -->

<!-- rnb-chunk-end -->


<!-- rnb-text-begin -->


*regression retweets*


<!-- rnb-text-end -->


<!-- rnb-chunk-begin -->


<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxucmV0d2VldHNfZ2xtIDwtZ2xtKFJldHdlZXQgfiAuLCBkYXRhPUJMTTIwMjApXG5gYGBcbmBgYCJ9 -->

```r
```r
retweets_glm <-glm(Retweet ~ ., data=BLM2020)

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiRXJyb3IgaW4gaXMuZGF0YS5mcmFtZShkYXRhKSA6IG9iamVjdCAnQkxNMjAyMCcgbm90IGZvdW5kXG4ifQ== -->

Error in is.data.frame(data) : object ‘BLM2020’ not found




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*One way ANOVA*


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```r
```r
#removing html tags from the source_tweet variable

library(textclean)
html_tags <- c(
    \<bold>Random</bold> text with symbols: &nbsp; &lt; &gt; &amp; &quot; &apos;\,
    \<p>More text</p> &cent; &pound; &yen; &euro; &copy; &reg;\
)

blm2020$source_tweet<- replace_html(blm2020$source_tweet)

<!-- rnb-source-end -->

<!-- rnb-output-begin eyJkYXRhIjoiRXJyb3IgaW4gYmxtMjAyMCRzb3VyY2VfdHdlZXQgOiAkIG9wZXJhdG9yIGlzIGludmFsaWQgZm9yIGF0b21pYyB2ZWN0b3JzXG4ifQ== -->

Error in blm2020$source_tweet : $ operator is invalid for atomic vectors




<!-- rnb-output-end -->

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*running the test*


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```r
```r
#creating a 

one.way <- aov(source_tweet ~ ., data = blm2020)

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Error in terms.formula(formula, , data = data) : ‘.’ in formula and no ‘data’ argument ```

#Graphs for every variable #Graph on user ammount of followers #Check if a user has made multiple tweets - how can we do that? #Filter if parties are mentioned

---
title: "BLM data analysis"
output: html_notebook
---

*Installing required packages*

```{r}
library(rtweet) 
library(tidyr)
library(ggplot.multistats)
library(ggplot2)
library(forestmangr)
library(syuzhet)
library(tidyverse)
library(readxl) # read excel
library(tibble) # tobble dataframe
library(dplyr) # piping
library(stringr) # character manipulation
library(tidytext)
library(tokenizers)
library(stopwords)
library(lobstr)
library(glue)

```
*Loading the data*

```{r}
blm2020<-read.csv('../../gen/data-preparation/temp/BLM2020_dataset.csv', sep = ' ', na.strings=c("", "NA"))

blm2021<-read.csv('../../gen/data-preparation/temp/BLM2021_dataset.csv', sep = '', na.strings=c("", "NA"))
```


*Show the ratio of replies, retweets and likes*

```{r}
#Retweets
blm2020_retweets<-blm2020[blm2020$retweet > 1, ]
blm2020_retweets
count(blm2020_retweets)
blm2021_retweets<-blm2021[blm2021$retweet > 1, ]
blm2021_retweets
count(blm2021_retweets)

```

```{r}
#Likes
blm2020_likes<-blm2020[blm2020$like >1, ]
blm2020_likes
count(blm2020_likes)
blm2021_likes<-blm2021[blm2021$like >1, ]
blm2021_likes
count(blm2021_likes)

```


```{r}
#Replies
blm2020_replies<-blm2020[blm2020$reply >1, ]
blm2020_replies
count(blm2020_replies)
blm2021_replies<-blm2021[blm2021$reply >1, ]
blm2021_replies
count(blm2021_replies)

```
*Creating a data frame*

```{r}

RLRdata2020<-data.frame(category=c("Retweets", "Likes", "Replies"), count=c(2, 5, 3))
RLRdata2020
RLRdata2021<-data.frame(category=c("Retweets", "Likes", "Replies"), count=c(707, 1738, 574))
RLRdata2021

# Maybe show results here as percentage of the total data? Makes more sense maybe since
# 2021 has more datapoints. This function:
# precentageRLR2020 <- RLRdata2020/blm2020 
```


*Visualizing the data 2020*

```{r}
#Adding columns
data2020$fraction = data2020$count / sum(data2020$count)
data2020$percentage = data2020$count / sum(data2020$count) * 100
data2020$ymax = cumsum(data2020$fraction)
data2020$ymin = c(0, head(data2020$ymax, n=-1))

#Rounding up

data2020 <- round_df(data2020, 2)

# Specify what the legend should say
Type_of_Tweet <- paste(data2020$category, data2020$percentage, "%")

ggplot(data2020, aes(ymax=ymax, ymin=ymin, xmax=4, xmin=3, fill=Type_of_Tweet)) +
  geom_rect() +
  coord_polar(theta="y") + 
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "right")

```
*Visualizing the data 2021*

```{r}

#Adding columns
data2021$fraction = data2021$count / sum(data2021$count)
data2021$percentage = data2021$count / sum(data2021$count) * 100
data2021$ymax = cumsum(data2021$fraction)
data2021$ymin = c(0, head(data2021$ymax, n=-1))

#Rounding up

data2021 <- round_df(data2021, 2)

# Specify what the legend should say
Type_of_Tweet <- paste(data2020$category, data2021$percentage, "%")

ggplot(data2021, aes(ymax=ymax, ymin=ymin, xmax=4, xmin=3, fill=Type_of_Tweet)) +
  geom_rect() +
  coord_polar(theta="y") + 
  xlim(c(2, 4)) +
  theme_void() +
  theme(legend.position = "right")

```




*Most frequent words in the 2020 tweets*

Lets see which words are more frequently used in the posts of 2020

```{r}
blm2020$text <- gsub("@\\S*", "", blm2020$text)
blm2020$text <- gsub("amp", "", blm2020$text) 
blm2020$text <- gsub("[\r\n]", "", blm2020$text)
blm2020$text <- gsub("[[:punct:]]", "", blm2020$text)

```


```{r}

#removing stop words from the text - so the most frequent words are not or at and

tweets <- blm2020 %>%
  select(text) %>%
  unnest_tokens(word, text)
tweets <- tweets %>%
  anti_join(get_stopwords(language = "nl", source = "snowball"))

```

```{r}
#making a histogram
tweets %>% # gives you a bar chart of the most frequent words found in the tweets
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
  labs(y = "Count",
       x = "Unique words",
       title = "Most frequent words found in the tweets of #BLM",
       subtitle = "Stop words removed from the list")
```
*Most frequent words in the 2021 tweets*
Lets now see which words are more frequently used in the posts of 2021 

```{r}
blm2021$text <- gsub("@\\S*", "", blm2021$text)
blm2021$text <- gsub("amp", "", blm2021$text) 
blm2021$text <- gsub("[\r\n]", "", blm2021$text)
blm2021$text <- gsub("[[:punct:]]", "", blm2021$text)

```


```{r}

#removing stop words from the text - so the most frequent words are not or at and

tweets1 <- blm2021 %>%
  select(text) %>%
  unnest_tokens(word, text)
tweets1 <- tweets1 %>%
  anti_join(get_stopwords(language = "nl", source = "snowball"))

```

```{r}
#making a histogram
tweets1 %>% # gives you a bar chart of the most frequent words found in the tweets
  count(word, sort = TRUE) %>%
  top_n(15) %>%
  mutate(word = reorder(word, n)) %>%
  ggplot(aes(x = word, y = n)) +
  geom_col() +
  xlab(NULL) +
  coord_flip() +
  labs(y = "Count",
       x = "Unique words",
       title = "Most frequent words found in the tweets of #BLM 2021",
       subtitle = "Stop words removed from the list")
```

*Sentiment analysis for positive or negative tweets 2020*

```{r}
# Converting tweets to ASCII to trackle strange characters
tweets_sentiment <- iconv(tweets, from="UTF-8", to="ASCII", sub="")

# removing retweets, in case needed 
tweets_sentiment <-gsub("(RT|via)((?:\\b\\w*@\\w+)+)","",tweets_sentiment)

# removing mentions, in case needed
tweets_sentiment <-gsub("@\\w+","",tweets_sentiment)
ew_sentiment<-get_nrc_sentiment((tweets_sentiment))
sentimentscores<-data.frame(colSums(ew_sentiment[,]))
names(sentimentscores) <- "Score"
sentimentscores <- cbind("sentiment"=rownames(sentimentscores),sentimentscores)
rownames(sentimentscores) <- NULL
ggplot(data=sentimentscores,aes(x=sentiment,y=Score))+
  geom_bar(aes(fill=sentiment),stat = "identity")+
  theme(legend.position="none")+
  xlab("Sentiments")+ylab("Scores")+
  ggtitle("Total sentiment based on scores 2020")+
  theme_minimal()

```
*Sentiment analysis for positive or negative tweets 2021*

```{r}
# Converting tweets to ASCII to trackle strange characters
tweets_sentiment_1 <- iconv(tweets, from="UTF-8", to="ASCII", sub="")

# removing retweets, in case needed 
tweets_sentiment_1 <-gsub("(RT|via)((?:\\b\\w*@\\w+)+)","",tweets_sentiment_1)

# removing mentions, in case needed
tweets_sentiment_1 <-gsub("@\\w+","",tweets_sentiment_1)
ew_sentiment<-get_nrc_sentiment((tweets_sentiment))
sentimentscores<-data.frame(colSums(ew_sentiment[,]))
names(sentimentscores) <- "Score"
sentimentscores <- cbind("sentiment"=rownames(sentimentscores),sentimentscores)
rownames(sentimentscores) <- NULL
ggplot(data=sentimentscores,aes(x=sentiment,y=Score))+
  geom_bar(aes(fill=sentiment),stat = "identity")+
  theme(legend.position="none")+
  xlab("Sentiments")+ylab("Scores")+
  ggtitle("Total sentiment based on scores 2021")+
  theme_minimal()

```



*Visualizing the variables in the dataset 2020*

```{r}
#Making a ggplot
#ploting retweets by location
ggplot(data = blm2020) + 
  geom_point(mapping = aes(x = retweet, y = location))
```
```{r}
#ploting retweets by datetime
#need to separate date from time
ggplot(data = blm2020) + 
  geom_point(mapping = aes(x = retweet, y = datetime), color = "blue")

```
*Graphing users amount of followers and user amount friends*

```{r}
ggplot(data = blm2020) + 
  geom_point(mapping = aes(x = user_amount_followers, y = user_amount_friends), color = "blue")

```

*Graphing users amount status by location*
To check how much users Tweet by Location
```{r}
ggplot(data = blm2020) + 
  geom_point(mapping = aes(x = user_amount_status, y = location), color = "blue")
```
#Filter if parties are mentioned in 2020

```{r}
state.name[grep ("VVD", "PvdA", "PVV", "CDA", "SP", "CU", "PvdD", state.name)]
```


*Visualizing the variables in the dataset 2021*
####needs amendments!!!!
```{r}
#Making a ggplot
#ploting retweets by location
ggplot(data = blm2021) + 
  geom_point(mapping = aes(x = retweet, y = location))
```
```{r}
#ploting retweets by datetime
#need to separate date from time
ggplot(data = blm2021) + 
  geom_point(mapping = aes(x = retweet, y = datetime), color = "blue")

```
*Graphing users amount of followers and user amount friends*

```{r}
ggplot(data = blm2021) + 
  geom_point(mapping = aes(x = user_amount_followers, y = user_amount_friends), color = "blue")

```

*Graphing users amount status by location*
To check how much users Tweet by Location
```{r}
ggplot(data = blm2021) + 
  geom_point(mapping = aes(x = user_amount_status, y = location), color = "blue")
```
#Filter if parties are mentioned in 2021

```{r}
state.name[grep ("VVD", "PvdA", "PVV", "CDA", "SP", "CU", "PvdD", state.name)]

# add "BIJ1" and "D66" -> I did see some tweets containing these. 
```

```{r}
#remove dollar signs #better to do this in cleaning? 

blm2020<-gsub("\\$", "", blm2020)
blm2021<-gsub("\\$","", blm2021)

```

*Sentiment analysis*

```{r}
# create a list of tokens
#https://www.kaggle.com/rtatman/tokenization-tutorial

tokens_2020<-data.frame(text= blm2020) %>% unnest_tokens(word, text)
tokens_2020
tokens_2021<-data.frame(text=blm2021) %>% unnest_tokens(word, text)
tokens_2021
```

Now that we have a list of tokens, we need to compare them against a list of words with either positive or negative sentiment.

```{r}
# get the sentiment from the first text: 
tokens_2020 %>%
  inner_join(get_sentiments("bing")) %>% # pull out only sentiment words
  count(sentiment) %>% # count the # of positive & negative words
  spread(sentiment, n, fill = 0) %>% # made data wide rather than narrow
  mutate(sentiment = positive - negative) # # of positive words - # of negative words

```
```{r}
#do the same for 2021

tokens_2021 %>%
  inner_join(get_sentiments("bing")) %>% # pull out only sentiment words
  count(sentiment) %>% # count the # of positive & negative words
  spread(sentiment, n, fill = 0) %>% # made data wide rather than narrow
  mutate(sentiment = positive - negative) # # of positive words - # of negative words

```


```{r}
blm2020$sentiment<-get_sentiment(blm2020$text)
blm2021$sentiment<-get_sentiment(blm2021$text)

```

Now we will perform an ANOVA with DV the sentiment of the text and control variable the source of the tweet

https://www.scribbr.com/statistics/anova-in-r/

```{r}
#2020
one.way_2020 <- aov(sentiment ~ source_tweet , data = blm2020)

summary(one.way_2020)
```
```{r}
#2021

one.way_2021 <- aov(sentiment ~ source_tweet, data = blm2021)

summary(one.way_2021)


```



#How can we measure how many tweets are made by location?

references:
1. https://towardsdatascience.com/a-guide-to-mining-and-analysing-tweets-with-r-2f56818fdd16
2. https://ourcodingclub.github.io/tutorials/time/
3. https://cran.r-project.org/web/packages/stopwords/stopwords.pdf
4. https://www.dummies.com/programming/r/how-to-search-for-individual-words-in-r/
5. https://r4ds.had.co.nz/data-visualisation.html
6. Anova and sentiment analysis: https://www.kaggle.com/rtatman/tutorial-sentiment-analysis-in-r
```{r}
sum(""(BLM2020))
sum(is.na(BLM2020$Datetime))
sum(is.na(BLM2020$Text))
sum(is.na(BLM2020$Tweet.Id))
sum(is.na(BLM2020$Username))
sum(is.na(BLM2020$Location))
sum(is.na(BLM2020$Location))
summary(BLM2020)
summary(BLM2020$Location)
```

*regression retweets*

```{r}
retweets_glm <-glm(Retweet ~ ., data=BLM2020)
```

*One way ANOVA*

```{r}
#removing html tags from the source_tweet variable

library(textclean)
html_tags <- c(
    "<bold>Random</bold> text with symbols: &nbsp; &lt; &gt; &amp; &quot; &apos;",
    "<p>More text</p> &cent; &pound; &yen; &euro; &copy; &reg;"
)

blm2020$source_tweet<- replace_html(blm2020$source_tweet)


```

*running the test*

```{r}
#creating a 

one.way <- aov(source_tweet ~ ., data = blm2020)

```


#Graphs for every variable 
#Graph on user ammount of followers
#Check if a user has made multiple tweets - how can we do that? 
#Filter if parties are mentioned